Dentistry, Faculty of
DSpace
UBCV
Dadoush, Maher
Master of Science - MSc
University of British Columbia
Background: Accurate localization of mandibular canals (MC) is important in Dental Implantology to avoid injuries of mandibular nerve inside the canal. Artificial intelligence (AI) has impacts on improving the quality of delivering Implantology. The aim of this study was to compare the accuracy of an AI system with conventional methods in detecting MC during pre-implant planning using cone-beam computed tomography (CBCT) images. Methods: A pilot study of six CBCT scans of a dentate human mandible was initially conducted to explore measurement agreements of manual tracing (coDiagnostiX), AI method (Diagnocat) and histology sections as gold standard. Measurements were conducted in 2nd premolars and 1st and 2nd molars areas bilaterally and coded as LM2, LM1, LP2, RP2, RM1, RM2. Subsequently, a retrospective study of 47 patients’ CBCT scans of their mandibles was analysed for MC localization by 2 examiners using coDiagnostiX, and Diagnocat. Measurements were taken from the midpoint of the superior aspect of the buccal and lingual cortex to the superior aspect of MC in the regions of interest (ROIs). The data was calibrated for intra-examiner and inter-examiner reliability. One-way ANOVA test was used to analyse three sets of measurements in pilot studies and independent samples t-tests were used in retrospective studies. Results: 110 CBCTs were screened, 57 CBCTs were excluded due to different ROIs, 2 CBCTs were excluded in coDiagnostiX due to old Digital Imaging and Communications in Medicine (DICOM) files and 4 CBCTs were excluded in Diagnocat due to metal artifact or extended ROI outside mandible area. Twelve CBCTs in AI software showed incidental findings including thickening of maxillary sinus membrane or dense bone islands in the mandible. No statistically significant difference found in the pilot study among the measurements of three groups except for RM2 (p<0.05) likely due to complex anatomy of the area and increased bone thickness. When sample size increased in the retrospective study, no statistically significant difference found between AI and coDiagnostiX in edentulous and dentate groups or both groups combined using independent samples t-test. Conclusion: AI software used in this study is as accurate and predictable as manual tracing software for MC detection.
Pre-Implant Tracing of Mandibular Canals on CBCT Images Using Artificial Intelligence by Maher Dadoush DDS, Damascus University, 2007 MSc O Med, Damascus University, 2012 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Craniofacial Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2023 © Maher Dadoush, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Pre-Implant Tracing of Mandibular Canals on CBCT Images Using Artificial Intelligence submitted by Maher Dadoush in partial fulfillment of the requirements for the degree of Master of Science in Craniofacial Science Examining Committee: Dr. David MacDonald – Professor, Department of Oral Biological and Medical Sciences, UBC Supervisor Dr. Babak Chehroudi – Clinical Professor, Department of Oral Health Sciences, UBC Supervisory Committee Member Dr. Bingshuang Zou – Associate Professor, Department of Oral Health Sciences, UBC Supervisory Committee Member Dr. Siddharth Vora – Assistant Professor, Department of Oral Health Sciences, UBC Additional Examiner iii Abstract: Background: Accurate localization of mandibular canals (MC) is important in Dental Implantology to avoid injuries of mandibular nerve inside the canal. Artificial intelligence (AI) has impacts on improving the quality of delivering Implantology. The aim of this study was to compare the accuracy of an AI system with conventional methods in detecting MC during pre-implant planning using cone-beam computed tomography (CBCT) images. Methods: A pilot study of six CBCT scans of a dentate human mandible was initially conducted to explore measurement agreements of manual tracing (coDiagnostiX), AI method (Diagnocat) and histology sections as gold standard. Measurements were conducted in 2nd premolars and 1st and 2nd molars areas bilaterally and coded as LM2, LM1, LP2, RP2, RM1, RM2. Subsequently, a retrospective study of 47 patients’ CBCT scans of their mandibles was analysed for MC localization by 2 examiners using coDiagnostiX, and Diagnocat. Measurements were taken from the midpoint of the superior aspect of the buccal and lingual cortex to the superior aspect of MC in the regions of interest (ROIs). The data was calibrated for intra-examiner and inter-examiner reliability. One-way ANOVA test was used to analyse three sets of measurements in pilot studies and independent samples t-tests were used in retrospective studies. Results: 110 CBCTs were screened, 57 CBCTs were excluded due to different ROIs, 2 CBCTs were excluded in coDiagnostiX due to old Digital Imaging and Communications in Medicine (DICOM) files and 4 CBCTs were excluded in Diagnocat due to metal artifact or extended ROI outside mandible area. Twelve CBCTs in AI software showed incidental findings including thickening of maxillary sinus membrane or dense bone islands in the mandible. No statistically significant difference found in the pilot study among the measurements of three groups except for RM2 (p<0.05) likely due to complex anatomy of the area and increased bone thickness. iv When sample size increased in the retrospective study, no statistically significant difference found between AI and coDiagnostiX in edentulous and dentate groups or both groups combined using independent samples t-test. Conclusion: AI software used in this study is as accurate and predictable as manual tracing software for MC detection. v Lay Summary This study evaluated the accuracy of an artificial intelligence (AI) system compared to conventional methods for detecting mandibular canals (MC) during dental implant surgery pre-planning using specialized three-dimensional x-rays. A total of 47 patients x-ray scans were analyzed by two examiners using both manual tracing and AI software. The data were coded relevant to the regions of interest and calibrated for inter-examiner reliability. The results indicated the AI software used in this study was as accurate and predictable as the manual tracing software for MC detection. Further, AI automated detection of MC saves time and improves implant placement safety by detecting MC and avoiding injury to the mandibular nerve inside the canal. vi Preface I conducted this pilot and retrospective clinical study under the indispensable guidance of my supervisor Dr. David MacDonald and my committee members Dr. Babak Chehroudi and Dr. Bingshuang Zou. I was responsible for collecting the data. The methodology was reviewed and planned under the guidance of Dr. David MacDonald. Human mandibles true measurements and all the scans and sectioning of the mandibles were conducted by Dr. Shamsher Sandlas for the pilot study. All DICOM files screenings and the measurements in the software in the pilot and retrospective study were conducted by myself. Dr. Babak Chehroudi helped me immensely with the statistical analysis. Ethics approval certificates were granted for all parts of this study from UBC Clinic Research Ethics Board (Certificate number: H22-00480) vii Table of Contents Abstract.......................................................................................................................................... iii Lay Summary...................................................................................................................................v Preface............................................................................................................................................vi Table of Contents .....................................................................................................................vii List of Tables................................................................................................................................ix List of Figures.................................................................................................................................xi List of Abbreviations ....................................................................................................................xiii Acknowledgments ........................................................................................................................xv Dedication ...................................................................................................................................xvii Chapter 1: Introduction………………………….........................................................................1 1.1 The Role of Cone Beam Computed Tomography (CBCT) in Implant Dentistry........................1 1.2 The Emergence of Artificial Intelligence Concept ……………………………………….....…4 1.3 Definition of Machine and Deep Learning …………………………………………….........…5 1.4 History of Utilizing AI in Healthcare …………………………………………………….....…6 1.5 AI Applications in Oral and Maxillofacial Radiology ………………………………...…...….7 1.6 AI Applications in Implant Dentistry 8 1.7 The Importance of Mandibular Canal Tracing and Detection During Implant Surgery……......8 1.8 Utilizing Artificial Intelligence Models for Mandibular Canal Tracing…………………..…...9 1.9 Clinical Significance of The Study…………………………………………………...……....12 1.10 Research Aims...................................................................................................................13 Chapter 2: Materials and Methodology......................................................................................14 2.1 The Pilot Study ........................................................................................................................14 2.2 The Retrospective Study ………………………………………………..…………………....21 2.3 CBCT Image Review and Mandibular Canal Tracing…………………...…………………...22 2.3.1 Manual Tracing of The Mandibular Canal ……………...………...…...………………...…22 2.3.2 Automatic Tracing of the Mandibular Canal……………………...…....……………….….23 2.4 CBCT Data Measurement……………………………………...…………………………….27 2.5 Statistical Analysis…………………………………………………………………………...32 Chapter 3: Results........................................................................................................................33 viii 3.1 Results of The Pilot Study........................................................................................................33 3.2 Results of The Retrospective study………………………………………………………......35 3.2.1 Data Evaluation ………………………...………………………………………………….35 3.2.2 Incidental findings from AI software………………………………………………………38 3.2.3 Comparison linear measurements of dentate and edentulous areas between Manual and AI software………………………………………………………….………….…...40 3.2.4 Comparison linear measurements of edentulous areas between Manual and AI software……………………………………………………………………………………….….42 Chapter 4: Discussion .................................................................................................................45 4.1 Related studies utilizing artificial intelligence models for Mandibular Canal Tracing…….....45 4.2 Comparison with previous studies…………………………………………………………...46 4.3 Limitations…………………………………………………………………………………...49 4.4 Future directions…………………………………………………………………………..….52 Chapter 5: Conclusion.................................................................................................................54 Bibliography.................................................................................................................................55Appendices....................................................................................................................................60 Appendix A....................................................................................................................................60 Appendix B ...................................................................................................................................70 Appendix C....................................................................................................................................72 ix List of Tables Table 1: Independent student t-test for total edentulous and dentate regions compared to AI and Manual tracing...............................................................................................................................41 Table 2: Independent student t-test for edentulous regions among AI and Manual software.......44 Table 3: Measured distance (in mm) corresponding to each scanned parameter and location in each of the Manual and AI software in the pilot study (First reading)..........................................60 Table 4: Measured distance (in mm) corresponding to each scanned parameter and location in each of the Manual and AI software in the pilot study (second reading)......................................61 Table 5: Distance measured (in mm) using digital calipers corresponding with the particular sections of interest in the dentate mandible in the pilot study.......................................................62 Table 6: Measured distance (in mm) corresponding to each location in the Manual software (coDiagnostiX) in the retrospective study (first reading)..............................................................62 Table 7: Measured distance (in mm) corresponding to each location in the Manual software (coDiagnostiX) in the retrospective study (second reading)..........................................................64 Table 8: Measured distance (in mm) corresponding to each location in the AI software (Diagnocat) in the retrospective study (first reading)....................................................................66 Table 9: Measured distance (in mm) corresponding to each location in the AI software (Diagnocat) in the retrospective study (second reading)...............................................................67 Table 10: Randomization of the study sample in the retrospective study using Microsoft word software..........................................................................................................................................70Table 11: Description of the total study sample............................................................................72 Table 12: Independent t-test for total edentulous and dentate regions compared to AI and Manual tracing in the retrospective study...................................................................................................72 x Table 13: Two tailed Independent t-test for total edentulous and dentate regions compared to AI and Manual tracing in the retrospective study...............................................................................73 xi List of Figures Fig. 1: Types of multiplanar reconstructions in CBCT datasets (a) the orthogonal MPR (b) the curved/panoramic MPR...................................................................................................................3 Fig. 2: A case displaying root resorption of the tooth adjacent to the maxillary impacted canine. (A) Orthogonal MPR. (B) Curved/panoramic MPR........................................................................4 Fig. 3: A drawing showing the different part of AI technology......................................................6 Fig. 4: Comparison of segmentation model and ground truth.......................................................10 Fig. 5: Comparison of different AI segmentation models and ground truth (Manual tracing)......11 Fig. 6: CBCT machine Carestream 9300 used for Mandible scan in the pilot study....................15 Fig. 7: During mandible scanning and fiducial markers positioning in the pilot study.................16 Fig. 8: Digital caliber used in the pilot study after sectioning of the mandible.............................16 Fig. 9: During manual tracing of MC and taking measurements of RM1 in coDiagnostiX..........17 Fig. 10: Linear measurements of RM1 region on coDiagnostiX...................................................18 Fig. 11:AI tracing of mandibular canal on Diagnocat software.....................................................19 Fig. 12: Linear measurements of RM1 on Diagnocat....................................................................20 Fig. 13: Linear measurements of RP2 on Diagnocat.....................................................................20 Fig. 14: A Flowchart of the pilot study..........................................................................................21 Fig. 15: coDiagnostiX software used for manual tracing of the mandibular canal...................... 22 Fig. 16: Diagnocat software used for automatic tracing of the mandibular canal.........................23 Fig. 17: A schematic drawing of the steps used by Diagnocat to do AI tracing of MC and Implantology report.......................................................................................................................25 Fig.18:A demo case in Diagnocat showing the basic AI study including orthodontic screening.26 Fig. 19: A demo case in Diagnocat showing basic AI study including Periodontal and xii Endodontic findings.......................................................................................................................26 Fig. 20: 3D model of mandible and teeth segmentation of the demo case in Diagnocat...............27 Fig. 21: During manual MC tracing and selection LM1 as ROI on coDiagnostiX.......................28 Fig. 22: Linear measurements of LM1 on coDiagnostiX..............................................................28 Fig. 23: During AI tracing of MC and implantology report on Diagnocat....................................30 Fig. 24: Linear measurements of LM1 on Diagnocat....................................................................31 Fig. 25: A Flowchart of the retrospective study............................................................................31 Fig. 26: Comparison between AI and Manual software measurements among different ROIs in the pilot study.................................................................................................................................34 Fig. 27: The difference between the Manual and AI measurements with gold standard measurements.................................................................................................................................35 Fig. 28: A chart of the total screened CBCTs with excluded DICOM files in both software......37 Fig. 29: One of the excluded cases due to metal artifact in the CBCT scan..................................37 Fig. 30: A Chart of the total incidental findings detected in Diagnocat........................................38 Fig. 31: Incidental findings in the mandible (Bone Island) as detected on Diagnocat..................39 Fig. 32: Incidental findings in the maxilla (Thickening of the maxillary sinus membrane) as detected on Diagnocat....................................................................................................................39 Fig. 33: Discontinuity in plotting the mandibular canal in AI software where this didn’t affect our linear measurements because it is not within the ROIs.................................................................40 Fig. 34: Comparison between AI and Manual software among total study sample......................42 Fig. 35: Comparison between AI and Manual software among ROIs...........................................43 xiii List of Abbreviations 2D – Two dimensional 2D SegNet – Two dimensional Segmentation Network 2D U-Net – Two dimensional U-shaped Architecture Network 3D – Three dimensional 3D U-Net – Three dimensional U-shaped Architecture Network AI – Artificial Intelligence ALADA – As Low As Diagnostically Acceptable ALARA – As Low As Reasonably Achievable ANOVA – Analysis of Variance CBCT – Cone Beam Computed Tomography CNN – Convolutional Neural Networks COVID-19 – Coronavirus Disease CT – Computed Tomography DICOM - Digital Imaging and Communications in Medicine DPR – Dental Panoramic Radiography DRL – Diagnostic Reference Level DTX – DTX Studio Implant FEA – Fine Element Analysis FOV – Field of View IAN – Inferior Alveolar Nerve kVp – Peak Voltage LM1 – Left First Molar xiv LM2 – Left Second Molar LP2 – Left Second Premolar mA – Milliamperage MC – Mandibular Canal ML – Machine Learning mm - Millimeter MPR - Multiplanar Reconstruction MRI – Magnetic Resonance Imaging OMFR– Oral and Maxillofacial Radiology PPE – Personal Protective Equipment PRIO – Periapical Radiolucency of Inflammatory Origin. RM1 – Right First Molar RM2 – Right Second Molar ROI – Region of Interest RP2 – Right Second Premolar SCT – Spiral Computed Tomography xv Acknowledgments I would like to express my sincere appreciation and thanks to my project supervisor, Dr. David MacDonald, for his invaluable guidance and mentorship throughout this project. Despite the common challenges and difficulties faced during graduate studies, the COVID-19 pandemic amplified these challenges, and Dr. MacDonald was extremely helpful in ensuring that we maintained a proper timeline and received necessary access to continue the project. His feedback was thorough and helped me grow both as a researcher and a clinician, and I am grateful for his support and guidance throughout this journey. I would also like to extend my gratitude to my committee members, Dr. Babak Chehroudi and Dr. Bingshuang Zou. Dr. Chehroudi played an instrumental role in establishing the methodology for this study and was always available to provide suggestions and feedback. He also helped immensely with setting up data analysis, and his quick and efficient responses to my concerns were greatly appreciated. Dr. Zou's contributions to data analysis were also essential in reaching the finish line. Her input was thoughtful and thorough, and her expertise greatly contributed to the success of the project. I would like to thank Mr. Wei Zhang from UBC dentistry IT department for his invaluable assistance for extracting DICOM files from UBC OHC database. His expertise with power tools was very valuable to this project. xvi Lastly, I want to express my deep appreciation to the Grad Perio faculty and staff, as well as my fellow co-residents. I am grateful to my clinical instructors for their time and dedication to mentoring and educating me, and their guidance and words of wisdom will always be remembered. To my co-residents, who have become like a second family, thank you for your support throughout this journey. Despite the ups and downs, we have managed to stay together and form lifelong relationships, and I will always cherish the time we spent together. xvii Dedication I dedicate this project to my beloved father, who passed away last year. He instilled in me a passion for learning and unwavering commitment to making a positive impact to the community. I also dedicate this degree to my beloved mother and brothers, who have been my unwavering source of support, love, and encouragement. To my friends and all those who believe in the power of science to build a better world, I dedicate this degree to you. Chapter 1: Introduction 1.1 The Role of Cone Beam Computed Tomography (CBCT) in Implant Dentistry After its initial introduction to dentistry in the 1990s, cone beam computed tomography (CBCT) has become a commonly used method of imaging in implant dentistry and periodontics (1). CBCT is an imaging technique that employs a conical beam of x-ray photons to generate a 3D image (2). The hardware of a CBCT machine consists of an X-ray tube, internal and external filtration, a fixed C-arm that allows for rotation in a horizontal plane, and an X-ray detector. The image is obtained as the X-ray tube rotates in a circular motion. During the rotation, the X-rays from the beam produce numerous two-dimensional projections that are captured by the detector. These images or projections are then utilized to construct a three-dimensional (3D) representation of the object (3). Although CBCT has gained widespread use, there is currently no universal standard for its variables and set parameters (3). It is crucial to recognize that the resulting image from CBCT relies on several variables such as mA, kVp, field of view (FOV), voxel size, and rotation arc (3). Altering one or more of these variables may impact both image quality and the amount of radiation exposure for the patient. Therefore, studies have been conducted to ensure that an adequate image for clinical diagnosis can be obtained while adhering to As Low As Reasonably Achievable radiation dose (ALARA principle). Recently, according to Widmann et al (4), the authors advocated the use of a new protocol of dose optimization which is As Low as Diagnostically Acceptable (ALADA principle) to reduce 2 the dose from CT scans to achieve a diagnostically acceptable lower dose that is substantially lower than the current diagnostic reference levels (DRLs) in CT scanners. CBCT offers several advantages over conventional dental radiography, including the ability to generate both 2D and 3D images (2). Moreover, it has superior spatial resolution compared to Spiral Computed Tomography (SCT) due to its isotropic cuberilles, while SCT has anisotropic cuberilles (2). The CBCT machine produces a 3D image by directly generating the cuberilles. Additionally, CBCT is less expensive than SCT and MRI and exposes patients to lower levels of radiation than SCT. However, CBCT is not as effective as MRI in capturing and displaying soft tissue. Nevertheless, this limitation is relatively unimportant in implant planning since the focus is on bone volume, density, and proximity to significant anatomical structures, such as the mandibular canal. It is worth noting that the American Academy of Periodontology (AAP) advocates using CBCT in implant planning to assess the location of critical anatomical structures and to communicate with surgical and restorative colleagues for the fabrication of surgical guides (5). There are two main types of reconstructions for a CBCT dataset: three-dimensional (3D) volumetric reconstruction and cross-sectional imaging. The standard type of cross-sectional imaging is called multiplanar reconstruction (MPR), specifically the orthogonal MPR which displays anatomical planes that are perpendicular to each other (axial, coronal, and sagittal). This is the most used type of reconstruction in previous studies. Other studies have used the curved or panoramic MPR, which was originally created for medical computed tomography and later adapted for dental implantology. Unlike the orthogonal MPR, which is automatically generated 3 upon opening the dataset, the curved/panoramic MPR requires the operator to plot out the dental arch on the axial reconstruction to generate the panoramic and trans-axial images (2,6). Fig. 1. Types of multiplanar reconstructions in CBCT datasets. (A) the orthogonal MPR. (B) the curved/panoramic MPR (Image courtesy from Dr. MacDonald) (6). Recently, MacDonald et al. (6) studied the cone-beam computed reconstructions in the evaluations of maxillary impacted canines to determine the effectiveness of using two different cross-sectional or multiplanar reconstructions (orthogonal and curved/panoramic) of CBCT datasets together in the diagnosis and treatment planning of maxillary impacted canines (Fig. 2). The results showed that both reconstructions were necessary to evaluate the presence or absence of root resorption of the teeth adjacent to the maxillary impacted canines and other features. 4 A B Fig. 2. A case displaying root resorption of the tooth adjacent to the maxillary impacted canine. (A) Orthogonal MPR. (B) Curved/panoramic MPR (Image courtesy from Dr. MacDonald) (6). 1.2 The Emergence of Artificial Intelligence (AI) Concept: Deep Learning and Big Data Analytics are two important fields in analytic data sciences. Big Data has a tremendous importance in our daily affairs where many corporations cowardly collect huge volume of data to analyze and process this information into impactful marketing decisions. Deep learning and AI methods work on using and learning big data to perform supervised or unsupervised modeling and propose decision outcomes without the need for exclusive human knowledge and intervention (7). 5 The concept of AI was first introduced by McCarthy in 1956 and indicated that AI is defined as ''the ability of a machine to mimic smart human performance to execute complicated jobs, such as problem solving, object and word recognition and decision-making” (8). 1.3 Definition of Machine and Deep Learning Machine learning (ML) is defined as a part of AI where computers are taught to perform self-acting future tasks after combining and analysing big sources of data. Machine learning algorithms improve as they are exposed to more information, and they do not rely only on pre-defined rules. Instead, they learn and promote by experience, using tremendous amounts of data to provide specific answers (9). Rule-based algorithms are complex manual methods used to address intricate issues. However, they lack the capability of ML algorithms, which can learn and recognize patterns from training data, allowing them to perform tasks similarly to humans. Consequently, the advancement of ML algorithms offers significant advantages in dental image analysis compared to relying solely on rule-based approaches (8). Deep learning is a part of machine learning that acts as the starting point for many AI tools used for analysing images. Deep learning employs convolutional neural networks (CNNs) which extract multiple features from successive layers of masks. CNNs are initially used for dealing with sophisticated and huge images and integrate numerous layers of backpropagation algorithms (9) (Fig. 3). 6 Fig. 3: A drawing showing the different parts of AI technology (9). 1.4 History of Utilizing AI in Healthcare Since the 1970s, numerous researchers have endeavored to utilize AI in healthcare. Within the medical domain, a considerable number of studies have focused on the implementation of AI for diagnostic purposes including the identification of lymph nodes, polyps, aneurysms, benign and malignant tumours, as well as bone age detection (9). The first study on AI in dentistry dates back to 1996. While research on oral and maxillofacial radiology is a recent development, most of them have focused on panoramic radiographs. However, it is essential to evaluate impacted teeth’s 3D positioning and their relationship with anatomical structures, which demands using 3D imaging techniques (9). 7 The growing utilization of 3D imaging methods in dentistry has promoted the development and application of AI algorithms for multiple clinical issues. CBCT and intraoral/facial scans afford image data that can be employed to establish 3D image-based AI systems, which can be used for automated diagnosis, treatment planning, and treatment outcomes prediction (10). 1.5 AI Applications in Oral and Maxillofacial Radiology In the field of oral and maxillofacial radiology (OMFR), machine learning algorithms discussed in the literature focused on three main applications: - Automated diagnosis of dental and maxillofacial illnesses where they study the correlation between input and output images. - Identification of anatomical landmarks for orthodontic and orthognathic treatment planning. Although a computer-aided digital tracing approach can be employed for 3D orthodontic analysis, orthodontists are still in need of manual identification of anatomical landmarks on several CBCT slices, where the manual localization process takes a long time, which discourages orthodontists from a fully digital workflow. - Enhancement of image quality in general (10). Abesi et al. (11) in a recent systematic review studied the diagnostic performance of the AI systems in OMFR region using CBCT images and concluded that the overall sensitivity of the AI models was 0.91and the overall positive predictive value for AI models was 0.93 and the pooled sensitivity of anatomical landmarks detections and segmentations were 0.88 and 0.92 respectively. 8 1.6 AI Applications in Implant Dentistry According to a systematic review by Revilla-León in 2021, the three main application of AI in implant dentistry were as follow: - Identification of implant type by using periapical and panoramic x-rays to recognize implant type with an overall accuracy rate between 93.8% to 98%. - Prediction of implant success or osseointegration prediction by using patient specific risk factors and ontology criteria with an accuracy rate for this prediction from 62.4% to 80.5% - Optimization of implant design by combining fine element analysis (FEA) calculations and AI algorithms and this demonstrated minimizing the stress at the implant-bone interface by 36.6% compared to using FEA only (12). 1.7 The Importance of Mandibular Canal Tracing and Detection During Implant Surgery The inferior alveolar nerve (IAN), which provides sensory innervation to the lower teeth and lower lips, is a critical structure within the mandible. In addition, after passing through the mental foramen, it is called the mental nerve, which supplies sensation to the chin area and lower lip (13). Precise identification of mandibular canals (MC) in lower jaws is crucial in dental implantology, where the position and size of the implant are determined nowadays manually by dental experts from 3D CT images to avoid harming the mandibular neurovascular bundle within the canal. Damage to IAN can lead to transient or permanent loss of sensation and discomfort for patients (14). 9 At present, there are many third-party software applications available like coDiagnostiX (Dental Wings, Canada), DTX Studio Implant (Nobel BioCare, Switzerland) and Simplant Pro (Dentsply Sirona, USA) that can not only view Digital Imaging and Communications in Medicine (DICOM) files but also provide additional options for planning implants. Over time, these software applications have become more advanced, with many of them featuring an "Implant library" that allows virtual placement of most of the implants available on the market in a selected trans-axial view. Additionally, most manual tracing software includes a "nerve marking" tool that can be free-handed or automatically generated to map out the mandibular canal's IAN. This is significant because newer software generations allow for planning and simulation before implant placement, and further advancements have led to tools that enable clinicians to create surgical guides for executing implant placement (15). The process of labelling mandibular canals is a time-consuming and labor-intensive task, so there is a requirement for a partially or fully automated system or tool that can assist clinicians in reducing their workload. However, there are certain challenges associated with automated segmentation using CBCT, including complex structures of the skull, a shortage of data sets, unclear edges, and noisy images (14). 1.8 Utilizing Artificial Intelligence Models for Mandibular Canal Tracing In 2020 Jaskari et al. utilized AI methods through deep learning algorithms to automatically segment the mandibular canal. In this study, they applied a fully convolutional neural network segmentation on a diverse dataset of 637 cone beam CBCT volumes, where radiologists coarsely annotated the mandibular canals. They used a separate dataset of 15 volumes with accurate voxel-level annotations of the mandibular canals to evaluate the model's performance (16). 10 The study found that their deep learning model, trained on the coarsely annotated volumes, accurately located the mandibular canals of the voxel-level annotated set. The mean curve distance and average symmetric surface distance were 0.56 mm and 0.45 mm, respectively, indicating a high level of accuracy as shown in Fig. 4. Fig. 4: Comparison of segmentation model and ground truth (manual tracing) (16). Another study by Kwak et al. in 2020 compared between different deep learning models for automatic mandibular canal detection. Experiments were carried out using work-in-progress automation software to develop a dental segmentation automation tool. The models used in the study were based on two-dimensional segmentation network (2D SegNet), two-dimensional U-shaped architecture network (2D U-Net), and three-dimensional U-shaped architecture network (3D U-Net) as preliminary research (17). Results showed that the 2D U-Net with adjacent images had a higher global accuracy of 0.82 compared to naïve U-Net variants. The 2D SegNet had the second highest global accuracy of 0.96, while the 3D U-Net exhibited the best global 11 accuracy of 0.99. These findings suggest that the 3D U-Net model has the potential to be an effective tool for dental segmentation automation as shown in Fig. 5. This 3D U-Net model is used by Diagnocat software which makes it the most accurate AI software available in the market so far. Fig. 5: Comparison of different AI segmentation models and ground truth (manual tracing) (17). 12 1.9 Clinical Significance of the Study CBCT from its first appearance in 1999 became essential in pre-implant planning. Although there are many software systems which can assist the operator, the actual process of detecting and tracing the mandibular canal can vary with the skill and experience of the operator. Such planning is aimed at maximising the use of the existing alveolar height, but also minimising the risk of injury to the mandibular canal and the neurovascular bundle it contains. This is where AI may play an important role as the accuracy of the final placement of the implant would be expected to enhance the end result, namely optimal restoration, and retention. Although this can only be proven by subsequent studies, an important first step is to access its feasibility, which will be provided by this study. 13 1.10 Research Aims This study has the following aims: • Evaluation and comparison of deep learning Artificial Intelligence Software (Diagnocat, California, USA) and manual tracing software (coDiagnostiX, Dental Wings, Canada) in detecting the mandibular canal on CBCT images during pre-implant planning. Our hypothesis is as follows: • Null hypothesis: There are no differences between the AI software (Diagnocat) and the manual tracing implant software (coDiagnostiX) in tracing and detecting the mandibular canal on CBCT images during implant surgery planning. • Alternative hypothesis: There are differences between the AI software (Diagnocat) and the manual tracing implant software (coDiagnostiX) in tracing and detecting the mandibular canal on CBCT images during implant surgery planning. 14 Chapter 2: Materials and Methodology 2.1 The Pilot Study A pilot study was conducted to compare manual, AI, and cadaver (gold standard) measurements of mandibular canal location using 6 CBCT scans of a dentate human mandible with a different pre-set range of voxel sizes and mA. These 6 CBCT scans were obtained from a previous study by Dr. Shamsher Sandlas. The purpose of Dr. Sandlas study was to evaluate the precision of four commonly used implant planning software programs (coDiagnostiX, DTX, Simplant, and BlueSkyPlan) by locating the mandibular canal using the freehand nerve marking tool. The study used two de-fleshed human mandibles to assess different areas of the mandible, including the canine, premolar, and molar regions. Gutta-percha was placed in the buccal and lingual aspects of the regions of interest, and the mandibles were scanned at varying resolutions and mA values. The resulting DICOM files were analyzed using the four implant planning software programs. The mandibles were then sectioned using a reverse-cutting saw, and a digital calliper was used to measure comparable areas for the gold standard measurements. The study concluded that varying the mA and voxel size values did not impact the accuracy of the four software programs, and CoDiagnostiX appears to be the most effective when using the most common settings for CBCT image acquisition. This is why we used this software in our study, as it is available in our premises and, according to Sandlas' study, it is the most accurate one compared to other manual tracing software (18). 15 Our pilot study used the same cadaver mandible to compare manual, AI, and cadaver (gold standard) measurements of mandibular canal location using 6 CBCT scans of the dentate mandible with a pre-set range of voxel sizes and mA. Permission from Dr. Shamsher Sandlas was obtained to use his data from the previous study where histological sections of the same regions of interest (ROI) were obtained and measured using a digital caliber. In addition, we used 6 CBCTs DICOM files for the dentate human mandible taken by Dr. Sandlas using Carestream 9300 CBCT machine (Carestream Dental LLC, Atlanta, GA) as seen in (Figs 6-8). Fig. 6: CBCT machine Carestream 9300 used for Mandible scan in the pilot study. (Image courtesy of Dr. Sandlas) (18). 16 Fig. 7: During mandible scanning and fiducial markers positioning in the pilot study (Image courtesy of Dr. Sandlas) (18). Fig. 8: Digital caliper used in the pilot study after sectioning of the mandible (Image courtesy of Dr. Sandlas) (18). 17 These 6 CBCT DICOM files were uploaded to Diagnocat and on coDiagnostiX for tracing of the mandibular canal. In coDiagnostiX, mandibular canals were manually traced, and linear measurements were performed in each selected region of interest (Figs 9 and 10). Fig. 9: During manual tracing of MC and taking measurements of RM1 in coDiagnostiX. 18 Fig. 10: Linear measurements of RM1 region on coDiagnostiX, measurements were taken from the midpoint of the superior aspect of the buccal and lingual cortex to the superior aspect of the mandibular canal. 19 In Diagnocat, an implantology report was used to allow the software automate tracing of the mandible and mandibular canal. Subsequently linear measurements were performed in each selected regions of interest (Figs 11-13). Fig. 11: AI tracing of mandibular canal on Diagnocat software. 20 Fig. 12: Linear measurements of RM1 on Diagnocat. Fig. 13: Linear measurements of RP2 on Diagnocat. Selected region of interest in the mandible were right second molar, right first molar, right second premolar, left second molar, left first molar, and left second premolar and coded as 21 follows: RM2, RM1, RP2, LM2, LM1 and LP2 and the linear measurements were performed from the upper border of the mandibular canal to the crest of the ridge. In the pilot study, linear measurements on coDiagnostiX and Diagnocat were compared with actual measurements on the dentate mandible (Gold Standard) using One-way Analysis of Variance (ANOVA) test and the flowchart is listed Fig. 14. Fig. 14: A Flowchart of the pilot study. 2.2 The Retrospective Study: • Application was made to the UBC Clinic Research Ethics Board and got approved (Certificate number: H22-00480). Mandible 6 CBCT ScansManual(CoDiagonstiX) 1&2 ReadingsMean CodiagnostiXAI(Diagnocat)1&2 ReadingsMean DiagnocatCadaver (Mandible) (Gold Standard)Actual MeasurementsSectioning 22 • A review of the UBC Oral Health Clinic (OHC) database was performed for CBCTs taken using the Carestream 9300 CBCT machine in the period of January 1st ,2010, to January 31st ,2020 in preparation for implant placement at UBC OHC. • Mr. Wei Zhang from UBC IT department helped us to extract these files based on specific treatment codes used for obtaining CBCT and provided me with a list of 110 patients CBCTs. • I did a screening for all these DICOM files and excluded small field and maxillary CBCTs. 2.3 CBCT Image Review and Mandibular Canal Tracing 2.3.1 Manual Tracing of the Mandibular Canal • The mandibular canal was traced using tracing software (coDiagnostiX) which utilizes the curved/panoramic MPR technique (Fig. 15). Fig. 15: coDiagnostiX software used for manual tracing of the mandibular canal. 23 • This involved aligning the imaging plane along the mandibular arch and creating a sequence of successive trans-axial images that were orthogonal to the curved planar reformation. These images were meant to have minimal distortion, which results in measurements with the least incident of error. • The images were not altered except for contrast and brightness adjustments, and the nerve was traced using the free-hand tool while viewing reformatted panoramic, sagittal, cross-sectional, and trans-axial images simultaneously. 2.3.2 Automatic Tracing of the Mandibular Canal • The mandibular canal was traced using specialized software (Diagnocat) that uses curved/panoramic MPR and AI deep learning technology (Fig. 16 and 17). Fig. 16: Diagnocat software used for automatic tracing of the mandibular canal. 24 • Diagnocat AI system prepares an implant planning report based on a pipeline of multiple pre-trained fully convolutional networks and algorithmic slice extraction to mask teeth and bone and prepare a panoramic reformatting to have a segmentation of the canal and the subsequent plotting of the IAN (18). • The Diagnocat AI system used pre-trained semantic segmentation networks that are based on a modified fully convolutional 3D U-Net architecture. • This allows the system to accurately identify and create segmentation masks for teeth and other anatomical features and approximate the location of missing teeth. • In order to plan dental implants, the system needs accurate segmentations of teeth, jaws, mandibular canals, maxillary sinuses, and missing teeth (19). 25 Fig. 17: A schematic drawing of the steps used by Diagnocat to perform AI tracing of MC and Implantology report (19). • The AI software has other features such as detection of the 3rd mandibular molar relation to the mandibular canal and detection of Periapical Radiolucencies of Inflammatory Origins (PRIOs) and dental caries but in our study, we used only the implant planning feature which is related to our research (Fig. 18-20). 26 Fig. 18: A demo case in Diagnocat showing the basic AI study including orthodontic screening. Fig. 19: A demo case in Diagnocat showing basic AI study including Periodontal and Endodontic findings. 27 Fig. 20: 3D model of mandible and teeth segmentation of the demo case in Diagnocat. 2.4 CBCT Data Measurement The digital measurement tools were used to take measurements in both coDiagnostiX (Fig. 21 and 22) and Diagnocat software (Fig. 23 and 24). In coDiagnostiX, a special feature was used to align the sagittal view and make the occlusal plane horizontal, while in Diagnocat, the occlusal plane was automatically aligned. For dentate areas, a straight line was drawn connecting the buccal and lingual segments in the RM2, RM1, RP2, LP2, LM1, LM2 regions of interest. Measurements were taken from the midpoint of the superior aspect of the buccal and lingual cortex to the superior aspect of the mandibular canal, determined by either free hand using the nerve marking tool or automatically by AI software. 28 Fig. 21: During manual MC tracing and selection LM1 as ROI on coDiagnostiX. Fig. 22: Linear measurements of LM1 on coDiagnostiX, measurements were taken from the top of the crest to the superior aspect of the mandibular canal. 29 In dentulous areas, measurements were taken from the top of the crest to the superior aspect of the mandibular canal, determined by free-hand nerve marking or automatic nerve tracing on RM2, RM1, RP2, LP2, LM1, LM2 regions of interest. In the edentulous mandible, we selected the mandibular foramen as a reference point for second premolar areas and we selected 6 different slices (1mm thickness each slice) between the foramen and first molar area and 12 slices between the mental foramen and the second molar area to help us determine the ROI. In the retrospective study, to avoid bias, intra-examiner and inter-examiner reliability was performed and my supervisor Dr. David MacDonald who is an experienced Oral and Maxillofacial Radiologist reviewed the linear measurements for each case in manual and AI software. I initiated a randomization group through Microsoft word software and that allowed us to randomly choose the cases in the research sample and we went through all the cases and measurements and if there is a disagreement in measurement, I correct it accordingly. The flowchart is listed in Fig. 25. 30 Fig. 23: During AI tracing of MC and implantology report on Diagnocat. 31 Fig. 24: Linear measurements of LM1 on Diagnocat. Fig. 25: A Flowchart of the retrospective study. CBCT Scans of the mandibleN = 47Manual(CoDiagonstiX) 1&2 ReadingsMean CoDiagnostiXAI(Diagnocat)1&2 ReadingsMean DiagnocatData Inter-Examiner Calibration Independent Student t-test 32 2.5 Statistical Analysis For data analysis and statistics, IBM SPSS statistics 28 (IBM Corporation, Armonk, NY, USA) was used. One-way ANOVA test was used in the pilot study to compare the means of the three groups (coDiagnostiX, AI, and Cadaver) and P-value set at (<0.05) and statistical difference is calculated below threshold value of the 95% confidence interval. An Independent student t-test was used to compare the means between the measurements of coDiagnostiX and AI and the difference in edentulous and dentate groups in the retrospective study and P-value set at (<0.05) and statistical difference is calculated below threshold value of the 95% confidence interval. 33 Chapter 3: Results 3.1 Results of the Pilot Study There was no statistically significant difference in the linear measurements between the three groups, voxel size, kVp, and mA as seen in Fig. 26 and we can exclude these variables as changing factors to the results since it is not showing any statistical difference whether we change the mA between 3.2, 4 or 5 or Voxel size between 180 or 300 and based on this data were pooled. Also, when we calculated the differences between the measurement values in Diagnocat and coDiagnostiX from the actual measurements of gold standard and there were no statistically significant differences between the regions of interest except in right second molar areas RM2 as seen in Fig. 27. It was concluded that automated measurements of locating the mandibular canal in AI software are as accurate as the gold standard (Cadaver measurements). The raw data tables for the pilot study are attached in the appendix A (subsection .1) including the first and the second readings. In addition, the gold standard tables are attached in the appendix A (subsection .2) as well. 34 Fig. 26: Comparison between AI and Manual software measurements among different ROIs in the pilot study. 35 Fig. 27: The differences between the Manual and AI measurements with gold standard measurements. 3.2 Results of the Retrospective Study 3.2.1 Data Evaluation All the images related to the corresponding sections mentioned in Chapter 2 were measured in both software. Each section in each software was measured twice. Each reading was taken at least 4-5 days apart to avoid bias. All measurements were reviewed and calibrated between 36 myself, and my research supervisor (Dr. David MacDonald) and the intra-examiner reliability was 99% of the cases the inter-examiner reliability was 98% of the cases. 110 patients’ CBCTs were screened and 57 DICOM files were excluded due to small field CBCTs or CBCTs of the maxilla which is not related to our study. Another 4 DICOM files were excluded from the sample because it was not opened correctly and not showing the automatic tracing of the mandibular canal in Diagnocat and this could be due to large FOV (10x10cm) or metal artifact within the CBCT scan. Also, 2 DICOM files were excluded from the sample because the files could not be opened in coDiagnostiX, and this could be due to old DICOM files being taken in 2013. As a result, the final sample number of DICOM files that were completely included in the total sample was 47 CBCTs. For 47 different patients with either 8cm x 8cm FOV or 10cm x 5cm FOV for the mandible as seen in Fig. 28 for more information. The raw data tables for the retrospective study are attached in the appendix A (subsection .3) including the first and the second readings of the AI and manual software. 37 Fig. 28: A chart of the total screened CBCTs with excluded DICOM files in both software. Fig. 29: One of the excluded cases due to metal artifact in the CBCT scan. Total DICOM files screened N=110 57 DICOM files excluded from total screened files due to capturing maxilla or small field CBCTs4 DICOM files excluded in Diagnocat due to enlarged FOV or metal artifact 2 DICOM files excluded in CoDiagnostix due to old files taken in 2013 38 3.2.2 Incidental Findings from AI Software: During our study, the AI software, which has the capability of detecting some abnormalities found in CBCT scans documented 12 patients out of 47 who had incidental findings on Diagnocat. 6 patients’ incidental findings were in the maxilla with maxillary sinus membrane thickening and 6 patients’ incidental findings were in the mandible with isolated bone islands (Fig. 30-32). Also, we noted in some cases a discontinuity in plotting the mandibular canal in AI software (Fig. 33) where this didn’t affect our linear measurements because it is not within the regions of interest. Fig. 30: A Chart of the total incidental findings detected in Diagnocat. Final DICOM files included N=4712 patients had incidental findings on Diagnocat6 patients incidental were in bone pathology in the mandible6 patients incidental were maxillary sinus pathology 39 Fig. 31: Incidental findings in the mandible (Bone Island) as detected on Diagnocat. Fig. 32: Incidental findings in the maxilla (Thickening of the maxillary sinus membrane) as detected on Diagnocat. 40 Fig. 33: Discontinuity in plotting the mandibular canal in AI software where this didn’t affect our linear measurements because it is not within the ROIs. 3.2.3. Comparison of Linear measurements of dentate and edentulous areas between Manual and AI software There were no statistically significant differences between manual and AI software measurements among regions of interest within the dentate and edentulous areas as shown in Table 1 and Fig. 34 and 35. 41 Table 1: Independent student t-test for total edentulous and dentate regions compared to AI and Manual tracing. Independent Student t-test Modality N Mean Std. Deviation Sig. t-value RM2 Diagnocat 47 14.8777 3.18363 0.907 -1.301 coDiagnostiX 47 15.7628 3.40746 RM1 Diagnocat 47 16.2372 3.64728 0.974 0.106 coDiagnostiX 47 16.1585 3.56764 RP2 Diagnocat 47 13.9681 3.01162 0.914 -0.363 coDiagnostiX 47 14.1904 2.93451 LP2 Diagnocat 47 14.9372 3.66916 0.708 -0.866 coDiagnostiX 47 15.6415 4.02987 LM1 Diagnocat 47 15.7713 3.89544 0.543 -0.088 coDiagnostiX 47 15.8394 3.56206 LM2 Diagnocat 47 13.7277 3.10919 0.377 -0.522 coDiagnostiX 47 14.0447 2.76797 42 Fig. 34: Comparison between AI and Manual software among total study sample. 43 Fig. 35: Comparison between AI and Manual software among ROIs. 3.2.4. Comparison of linear measurements of edentulous areas between Manual and AI software Independent student t-test was conducted to compare linear measurements of edentulous areas among region of interests between manual and AI software. There were no statistically significant differences between manual and AI software linear measurements among regions of interest in the edentulous areas as shown in Table 2. 44 Table 2: Independent student t-test for edentulous regions among AI and Manual software. Independent Student t-test Modality N Mean Std. Deviation Sig. t-value RM2 Diagnocat 20 12.9925 3.23863 0.773 -0.801 coDiagnostiX 20 13.8350 3.41425 RM1 Diagnocat 23 14.5304 3.48186 0.676 -0.195 coDiagnostiX 23 14.7304 3.48196 RP2 Diagnocat 13 11.6192 2.65476 0.624 -0.107 coDiagnostiX 13 11.7269 2.46768 LP2 Diagnocat 15 11.5533 2.60106 0.949 -0.520 coDiagnostiX 15 12.1233 2.57334 LM1 Diagnocat 27 14.6074 3.62315 0.697 -0.287 coDiagnostiX 27 14.8796 3.33280 LM2 Diagnocat 24 14.3688 4.26441 0.941 -0.603 coDiagnostiX 24 15.0354 4.60967 45 Chapter 4: Discussion There were some publications in the literature that traced the mandibular canal automatically. In 2016, a study attempted to demonstrate an algorithm-based automated method for detecting the mandibular canal through image reconstruction. They found that the mean distance error between their automated curve and the gold standard was less than 1mm (20). The gold standard, in their study, was established through prior agreement on the mapping of the mandibular canal as compared to the automated curve generated by the software. It would be worthwhile to further evaluate the accuracy of the computer-generated curve compared to the gold standard using digital callipers. The current study utilized reconstructed dental panoramic radiography (DPR) from CBCT and trans-axial, cross-sectional, and sagittal views to trace the mandibular canal for reproducibility. Gerlach et al. (21) compared the reproducibility of tracing the mandibular canal using reconstructed DPR images and coronal views. They found that the best reproducibility was achieved when the two tracing methods were combined and suggested a safety margin of 1.7mm in all directions as they observed a 95th percentile deviation of 1.3mm with a standard deviation of approximately 0.4mm. 4.1 Advantages of Automatic Tracing of Mandibular Canal and AI Software in Dentistry Identifying the IAN is a crucial step in dental treatment planning and other oral and maxillofacial surgeries. However, in cross-sectional CBCT images, the IAN is typically identified manually, which is time-consuming and prone to error. CBCT has limitations compared to CT scans, such as lower contrast and accuracy in measuring density. The shape of the mandibular canal is 46 difficult to determine precisely in a single direction due to its complex 3D trajectory and changes in position with head movements. Identifying the canal's shape may be challenging if the cortical layer surrounding the mandibular canal is thin or the medulla pattern is unclear. Furthermore, distinguishing the mandibular canal from overlapping or adjacent teeth can be challenging. Finally, the process of manually annotating the canal in each cross-sectional image at the pixel level is time-consuming (16,22,23). We noticed in the results of our study that the majority of manual measurements were slightly higher than the measurements of AI software. However, this slight difference is not statistically or clinically significant. Still, it suggests that AI software could potentially provide safer measurements to protect the IAN and avoid any injury to the nerve inside the canal. Having an accurate and reliable AI system that automatically detects the mandibular canal would be beneficial to dental implant clinicians and non-clinicians working closely in implant dentistry planning, such as dental technicians and radiologists. They could rely on such an AI system to reduce the time spent on preparation for implant planning and fabrication of guided implant templates. Additionally, it could reduce the extensive training required for manual tracing and learning to accurately trace and detect the mandibular canal. Ensuring the safe placement of the implant and avoiding injury to the mandibular nerve would be facilitated by the accurate performance of implant planning and mandibular canal tracing through the use of the AI system. In addition, this could help clinicians and non-clinicians to coincidentally detect bone abnormalities and pathologies during implant planning with the aid of AI software where can prevent advanced and extensive pathologies from happening by early detection and early referral to specialized clinicians. 47 4.2 Comparison with previous studies According to Sandlas et al. (18) who compared different manual tracing software to dentate and edentulous human mandible measurements as gold standard, he found there are no statistically significant difference in the accuracy of linear measurements between the four manual tracing software and gold standard. Our study agrees with the previous study with no statistically significant differences were found in the linear measurements between AI software (Diagnocat) and manual tracing software (coDiagnostiX). In Kwak et al.'s study (17), it was found that the CNN method (AI system) could accurately determine the mandibular canal in CBCT images, which may hold potential for future dental planning. Similarly, Jaskari et al. (16) employed the CNN method (AI system) to segment the mandibular canal in CBCT images. The authors of these studies noted that AI systems can provide precise and trustworthy results in canal identification, and these systems may play an essential role in future implant planning. It is worth noting that 2D-UNet is used for DPRs that require 2D interpretation, whereas 3D-UNet is used for 3D images for volumetric interpretation (17). Moreover, terms such as "Ground truth" and "gold standard" are used in various fields, including medical imaging and machine learning, to refer to reference or benchmark data considered accurate and reliable for comparison and evaluation purposes. Ground truth refers to the definitive, objective, and unbiased data that serve as the true representation of the target or the correct labeling of the data. It results from meticulous and precise methods, often established by experts or manual analysis. Ground truth data are used to 48 validate and assess the performance of algorithms or models by comparing their results against the established truth. Gold standard is a term used interchangeably with ground truth, indicating the highest level of accuracy and reliability in a specific context. It may be derived from histological criteria or other established and accepted criteria. The gold standard serves as a benchmark to evaluate the performance of new algorithms or techniques (16). Bayrakdar et al. (19) assessed the accuracy of an artificial intelligence (AI) system in implant planning using CBCT images. The study included 75 CBCT images, and the bone height and thickness were measured manually by a human observer in 508 regions where implants were needed. Additionally, canals, sinuses, and missing tooth regions were identified. The measurements were then repeated using AI system. The study found that the AI system was comparable to the manual measurements in terms of bone height measurements in certain regions but there were significant differences in bone thickness measurements in all regions of the mandible and maxilla. The AI system had a high percentage of correct detection for missing tooth regions and moderate percentage for canals and sinuses/fossae. Our study agrees with the previous studies that AI system is comparable to manual measurements in terms of bone height measurements and explains the difference found in the second molar areas in the pilot study where the bone thickness is increased, and AI system might not be able to detect the correct thickness of bone and implant diameter in those areas. 49 4.3 Limitations While we try to prevent mistakes, it's inevitable that there will be some human errors. One reason for discrepancies at certain locations could be because of measurement errors, which may occur if the cross-section and trans-axial sections on the CBCT doesn't align with the anatomical cross-sections. It is worth mentioning that each case of the 47 out of 110 patients required tremendous efforts to be included in the study. The process involved encrypting the data sent to a cloud-based software (Diagnocat) for confidentiality purposes, along with the time consumed for uploading the DICOM files and ensuring the orientation of the tomographs was ideal for manual tomography. Therefore, having these 47 cases wasn't that easy, especially considering that all CBCTs had different kVp and Amps, and we worked hard to extract the right data for our study and confirm that these DICOM files could be read on AI software and manual software. For the retrospective study, one person conducted the measurements, and another person revised and calibrated the measurements, in addition to reviewing the measurements made by the initial person who conducted the study. This approach aimed to minimize measurement biases by ensuring consistency throughout the study. Moreover, accuracy and precision are two important concepts used to measure the performance and reliability of a measurement or a predictive model. They are often used in various fields such as science, engineering, statistics, and machine learning. 50 Accuracy refers to how close a measurement or prediction is to the true or target value. In other words, it assesses the correctness of the result. It is calculated as the number of correct predictions or measurements divided by the total number of predictions or measurements. Precision, on the other hand, measures the reliability and consistency of the results. It evaluates the ability of a measurement or a model to provide the same result when repeated under the same conditions. Precision is calculated as the number of true positive predictions (correctly identified positive instances) divided by the total number of positive predictions (16). The aim was to assess the accuracy and reliability of the measurements to a certain degree. As using and manipulating the software requires a specific level of knowledge and skill, having someone who is not familiar with all aspects of the software could potentially introduce bias to the results. In addition, this study retrospectively compared two software programs without using actual measurements on the mandible, which is not realistic given that it pertains to humans. To address this, we conducted a pilot study involving human mandibles to establish a basic comparison framework and simulate it in the retrospective study. Moreover, the use of artificial intelligence (AI) in dentistry has undoubtedly brought numerous advancements and benefits. AI has the potential to reduce time and provide a valuable second opinion, particularly in diagnostic processes. However, it is important to approach the idea of AI replacing specialists with caution and recognize that such a view may not be entirely safe or realistic (29). 51 AI can excel at analyzing vast amounts of data and identifying patterns that might be missed by human specialists. It can assist in areas such as medical imaging interpretation, symptom analysis, and even treatment recommendations based on evidence-based guidelines. In this capacity, AI can certainly provide valuable insights and enhance the capabilities of healthcare professionals (29). Furthermore, it is important to note that while AI has access to vast amounts of data, it lacks the ability to establish rules on its own. ML algorithms are designed to perform specific tasks, and their performance improves with more information. Therefore, it is crucial to consider whether the way in which the task is executed aligns with the best practices defined by a human specialist or not. However, there are certain aspects of dental practice that require human judgment, empathy, and expertise, which AI may not fully replicate. Dentistry is a complex field that involves not only clinical knowledge but also the ability to consider a patient's unique circumstances, preferences, and values. The decision-making process often involves weighing various factors and considering the context of an individual's health (8). In light of these considerations, it is important to view AI as a tool that complements and supports specialists rather than a complete replacement. By leveraging AI as a second opinion, specialists can benefit from the additional insights and perspectives it offers, leading to more accurate diagnoses and optimized treatment plans. Collaboration between AI systems and human specialists can create a synergistic approach that combines the strengths of both (8). 52 Furthermore, the AI software encountered issues with certain DICOM files that had a larger field of view or contained metal artifacts. Additionally, some cases showed a discontinuity in mandibular canal tracing, which the manufacturer attributed to processing errors or the fact that the mandibular canal is notionally a line and does not wave back and forth from the medial mandibular canal to the buccal mental foramen due to the catenary curve. Hence, its journey is more in the vertical plane, which might be another cause of the discontinuity. Fortunately, this discontinuity didn't affect our study because it was outside ROIs. The developer is currently working on improving the software by expanding and refining its training to make it more accurate and capable of handling various scenarios. Despite involving only 47 patients, it's worth mentioning that this retrospective study had more than 1100 data points, which is comparable to or better than some similar studies on accuracy conducted in the past, such as those by Sandlas (18), Neves et al. (24), Vasconcelos et al. (25), Fokas et al. (26) and Bayrakdar et al. (19). 4.4 Future directions: The study found these two software programs had remarkably high accuracy for all the region of interests examined, but it's important to note that only mandibles were used in this study. It could be repeated with the maxilla to determine if the results are consistent. Another study by Luangchana et al. (27) found greater discrepancies between CBCT and anatomical measurements in the maxilla compared to the mandible. It's also worth noting that the dentate and edentulous mandibles used in this study did have metal objects or restorations. Such objects tend to introduce scatter and may result in suboptimal 53 imaging, particularly in the area of implant planning. It is therefore better to control more of the study sample and select patients without metal restorations or any object that could lead to scatter in the images and could lead to different linear measurements although in Ismail's study (28) he didn’t find a statistically significant difference if there is a metal object introduced in the CBCT image to affect linear accuracy, but that study was on pig jaws and had specific limitations. Moreover, this study compared the two software programs based on curved/panoramic MPR. It would be worthwhile to conduct a similar study using orthogonal MPR and image subtraction techniques to compare the accuracy of the two programs as conducted in Jaskari et al.'s study (16). 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Jaskari J, Sahlsten J, Järnstedt J, Mehtonen H, Karhu K, et al. Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes. Sci Rep. 2020;10:5842. https://doi.org/10.1038/s41598-020-62321-3 17. Kwak G, Kwak E, Song J, Park H, et al. Automatic mandibular canal detection using a deep convolutional neural network. Sci Rep. 2020;10(1):5711. https://doi.org/10.1038/s41598-020-62586-8 18. Sandlas S. (2021). Accuracy of commercially available implant planning software (T). University of British Columbia. Retrieved from https://open.library.ubc.ca/collections/ubctheses/24/items/1.0398222. 19. Bayrakdar S, Orhan K, Bayrakdar I, Bilgir E, Ezhov M, Gusarev M, Shumilov E. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging. 2021;21:86. https://doi.org/10.1186/s12880-021-00618-z 20. Bahrampour E, Zamani A, Kashkouli S, Soltanimehr E, Ghofrani Jahromi M, Sanaeian Pourshirazi Z. Accuracy of software designed for automated localization of the inferior alveolar nerve canal on cone beam CT images. Dentomaxillofac Radiol. 2016;45(2):20150298-20150298. https://doi.org/10.1259/dmfr.20150298 21. Gerlach NL, Meijer GJ, Maal TJ, Mulder J, Rangel FA, Borstlap WA, Bergé SJ. Reproducibility of 3 different tracing methods based on cone beam computed tomography in determining the anatomical position of the mandibular canal. Int J Oral Maxillofac Implants. 2010;68(4):811–817. https://doi.org/10.1016/j.joms.2009.09.059 58 22. Bryant R, MacDonald-Jankowski DS, Kim K. Does the type of implant prosthesis affect outcomes for the completely edentulous arch? Int J Oral Maxillofac Implants. 2007;22(Suppl):117-139. 23. MacDonald D. Cone-beam computed tomography and the dentist. J Investig Clin Dent. 2015 Jul 14. Doi: 10.1111/jicd.12178. 24. Neves FS, Vasconcelos TV, Campos PS, Haiter-Neto F, Freitas DQ. Influence of scan mode (180°/360°) of the cone beam computed tomography for preoperative dental implant measurements. Clin. Oral Impl. Res. 2014;25(2):e155–e158. https://doi.org/10.1111/clr.12080 25. Vasconcelos TV, Neves FS, Moraes LB, Freitas DQ. Vertical bone measurements from cone beam computed tomography images using different software packages. Braz Oral Res. 2015;29(1):1-6. https://dx.doi.org/10.1590/1807-3107BOR-2015.vol29.0035 26. Fokas G, Vaughn VM, Scarfe WC, Bornstein MM. Accuracy of linear measurements on CBCT images related to presurgical implant treatment planning: A systematic review. Clin. Oral Impl. Res. 2018;29(Suppl 16):393–415. https://doi.org/10.1111/clr.13142 27. Luangchana P, Pornprasertsuk-Damrongsri S, Kiattavorncharoen S, Jirajariyavej B. Accuracy of linear measurements using cone beam computed tomography and panoramic radiography in dental implant treatment planning. Int J Oral Maxillofac Implants. 2015;30(6):1287–1294. https://doi.org/10.11607/jomi.4073 28. Ismail A. Measurement accuracy in cone beam computed tomography in the presence of metal artifact (T). University of British Columbia. 2020. Retrieved from https://open.library.ubc.ca/collections/ubctheses/24/items/1.0392375. 59 29. Lerner H, Mouhyi J, Admakin O, Mangano F. Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health. 2020;20:80;2-3. https://doi.org/10.1186/s12903-020-1062-4 60 Appendices Appendix A A.1 Data tables for the pilot study – First reading and second reading Tables 7 and 8 represent the measured distances in AI and manual software with the corresponding scanning parameters. Scanning parameters are represented by mA, kVp and voxel sizes. Each measured distance also corresponds to its location. Tables 7 and 8 represent the first and second reading of the pilot study. Table 3: Measured distance (in mm) corresponding to each scanned parameter and location in each of the Manual and AI software in the pilot study. Dentate Mandible First reading Vol. mA kVp voxel size RM2 RM1 RP2 LP2 LM1 LM2 coDiagnostiX 20 3.2 90 180 22.2 22 16.7 17 22.6 22.2 23 4 90 180 22.7 21.8 16.1 17.4 22.5 22.8 24 5 90 180 22.8 21.4 16.5 17.4 22.8 22.9 25 3.2 90 300 22.8 21.3 16.9 17.7 23.3 22.4 26 4 90 300 22.2 23 16.3 17.4 23.2 22.5 27 5 90 300 23.2 21.7 16.6 17.2 23.4 23.8 Diagnocat AI 20 3.2 90 180 21.89 22.07 16.81 17.05 23.73 22.22 23 4 90 180 21.62 21.79 16.69 17.11 23.45 22.37 24 5 90 180 21.07 22.31 16.4 17.75 24.19 22.3 61 25 3.2 90 300 21.78 21.95 16.09 17.1 23.67 22.79 26 4 90 300 21.75 23.06 16.64 17.2 23.53 22.4 27 5 90 300 21.3 21.94 16.7 17.44 23.5 22.24 Table 4: Measured distance (in mm) corresponding to each scanned parameter and location in each of the Manual and AI software in the pilot study. Second reading Vol. mA kVp voxel size RM2 RM1 RP2 LP2 LM1 LM2 coDiagnostiX 20 3.2 90 180 22.1 23 16.4 16.5 23.7 22.6 23 4 90 180 22.3 22.3 16 16.9 23.5 22.3 24 5 90 180 21.7 22.4 16.3 17.3 24 23 25 3.2 90 300 22.3 22 15.7 17.3 24.2 22.2 26 4 90 300 21.7 21.4 16.6 17.6 23.3 21.5 27 5 90 300 21.7 22.5 17 17 23.5 22.9 Diagnocat AI 20 3.2 90 180 21.15 21.56 16.47 17.76 23.95 22.15 23 4 90 180 21.31 22.37 16.49 17.24 23.28 22.08 24 5 90 180 21.39 22.3 16.42 17.34 23.83 22.15 25 3.2 90 300 21.22 22.4 16.39 17.58 23.78 22.49 26 4 90 300 21.5 22.62 16.67 16.8 23.45 22.11 27 5 90 300 21.14 22.42 16.3 17.59 24.21 22.42 62 A.2 Digital Caliper / “Gold standard” measurements for the pilot study Table 9 represents the caliper readings in millimeter for the dentate Mandible corresponding to the particular sections of interest. Table 5: Distance measured (in mm) using digital calipers corresponding with the particular sections of interest in the dentate mandible in the pilot study. Mandible 1 Location Distance (in mm) RM2 21.23 RM1 22.54 RP2 16.5 LP2 17.28 LM1 23.29 LM2 22.78 A.3 Data tables for the retrospective study – First reading and second reading Tables 10 to 13 represent the measured distances in AI and manual software with each corresponded location. Table 6: Measured distance (in mm) corresponding to each location in the Manual software (coDiagnostiX) in the retrospective study (first reading). coDiagnostiX First reading Research Nr. Assigned number RM2 RM1 RP2 LP2 LM1 ML2 R001 33761 21.7 21.7 15.4 14.3 20.3 20.6 R002 70758 14.2 15.3 16.6 18.2 16.6 13.8 R003 71318 19.7 20.8 15.7 19.3 18.2 19.4 R004 78722 17.5 10.8 10.7 12.8 10 16.1 R005 99972 16.4 17.8 17.9 16.4 15.9 15.3 63 R006 101250 12.2 11 9.9 11.6 11.3 11.7 R007 106525 12.8 12.4 13.4 15.8 12.9 12.6 R008 107751 15.2 16.1 11.6 16.2 18.6 20.1 R009 110879 20.9 22.6 17 15.3 21 21.4 R010 114531 8.2 8.9 10.8 7.1 9.1 9.5 R011 114562 16.3 17.1 17.7 17.1 15.3 14.5 R012 114629 16.4 17 16.4 15.6 18.5 17.3 R013 116629 14.2 15.1 13.5 11.6 16.8 14 R014 119284 16.7 16.2 18.4 16.2 15.9 18.4 R015 119873 Excluded ROI R016 120187 16.1 15.8 15.2 15.1 14.9 15.4 R017 120606 12.2 13.2 12.9 13.5 14.7 14.8 R018 121708 15.4 15.3 13.2 14 16.8 14.5 R019 122247 16.9 17.5 18.9 15.5 14.5 13.4 R020 122814 14.4 13.9 18.2 11.8 14.8 15 R021 122897 20.1 23.2 13.8 13.8 25 25.7 R022 47791 Excluded old R023 72860 Excluded maxilla R024 75109 Excluded small f R025 77535 Excluded old R026 81192 16.6 20.2 14.4 18.2 18.9 18 R027 109848 16.3 17 15.3 10.9. 11.3 13.8 R028 110879 20.6 21.7 16.3 15.4 21.7 21.4 R029 113559 16.5 18.2 15 15.7 18 17.4 R030 114246 16.4 17.5 11.7 13.7 16.7 18.1 R031 114248 14.6 16 12.3 12.6 15.6 14.7 R032 118059 15.8 16.6 12.7 12.5 17.1 14.9 R033 119190 15.5 15.6 12.5 11.6 12.8 11.4 R034 119324 22.3 18.8 24 18.4 17.5 19.8 R035 119420 17 17.6 14.3 13.4 16.4 15.1 R036 120604 10.6 12.6 13.7 13.1 13.5 12.1 R037 121353 9.4 10.7 12.4 12.1 12.8 12.5 R038 121681 18.2 18.5 15 15.3 14.1 17.5 R039 122382 17.7 18.4 14.6 13.8 19.2 18.5 R040 31707 11.7 10.9 10.8 10.5 10.8 10.4 R041 52056 12 12.4 11.2 9 9.4 7.8 R042 70758 14.1 15.1 14 14.9 15.5 13.9 R043 89270 20.6 21.2 14.8 13.3 14.6 13 R044 89446 5.1 3.6 5.8 4.1 4 5.9 64 R045 90496 15.9 16.4 10.7 15.9 22.3 23 R046 98645 12.8 12.7 13 11.4 13.5 13.9 R047 100548 17.9 17.9 15.3 17.3 15.9 14.6 R048 101256 8.1 10.5 12.1 10.9 11.3 5.8 R049 102366 7 6.8 5.7 6.8 8.5 6.8 R050 109381 16.2 16.7 14.1 14.9 17.1 16.7 R051 111277 15.6 16.7 14.7 16.5 20.3 17.9 R052 112825 11.8 12.9 12.4 14.8 15.5 16.1 R053 117847 21 17.9 15.2 12.3 15.2 15.7 R054 121047 16.1 18.4 13.5 15 19.3 17.9 R055 122193 17.6 17.1 14.6 16.1 16.7 19.9 R056 122805 17.6 17.1 15 12.7 17.2 17.7 Table 7: Measured distance (in mm) corresponding to each location in the Manual software (coDiagnostiX) in the retrospective study (second reading). coDiagnostiX Second reading Research Nr. Assigned number RM2 RM1 RP2 LP2 LM1 ML2 R001 33761 21.5 21.8 15.3 14.1 20.4 20.7 R002 70758 14.1 15.2 16.4 18.3 16.5 13.4 R003 71318 19.9 20.5 15.4 19.1 18.3 19.8 R004 78722 17.2 10.4 10.9 13 10.3 15.8 R005 99972 16.4 18.2 17.3 16.5 16.3 15.2 R006 101250 12.2 11.3 9.8 11.4 11.2 11.7 R007 106525 12.7 12.5 13.8 15.8 12.9 12.6 R008 107751 15.3 16.2 11.3 16.3 18.6 19.9 R009 110879 20.8 22.4 17.2 15.3 21.3 21.5 R010 114531 8.3 8.7 10.6 7.2 9.2 9.4 R011 114562 16.2 17.2 17.8 17.4 15.4 14.3 R012 114629 16.2 17.8 16.2 16.1 18.3 17.3 R013 116629 14.1 15.6 13.7 11.7 17.2 14.2 R014 119284 16.7 16.3 18.2 16.3 15.4 18.2 R015 119873 Excluded R016 120187 16.3 15.4 15.3 15.2 14.8 15.3 R017 120606 12.1 13.1 12.7 13.3 14.3 14.7 R018 121708 15.1 15.1 13.6 13.6 16.4 14.3 R019 122247 16.9 17.7 18.8 15.8 14.3 13.5 R020 122814 14.2 13.4 18.8 11.6 14.7 15.2 65 R021 122897 20.2 23.1 13.4 13.5 25.3 25.7 R022 47791 Excluded R023 72860 Excluded R024 75109 Excluded R025 77535 Excluded R026 81192 16.5 20.1 14.2 18.1 18.7 18.1 R027 109848 16.4 17.5 15.4 10.7. 11.1 13.5 R028 110879 20.3 21.7 16.2 15.5 21.6 21.3 R029 113559 16.5 18.2 15.4 15.7 18 17.2 R030 114246 15.1 17.8 11.9 13.4 16.2 18.4 R031 114248 14.2 16.8 12.1 12.3 15.3 14.4 R032 118059 15.3 16.2 12.3 12.3 17.3 14.3 R033 119190 15.7 15.6 12.3 11.4 12.8 11.1 R034 119324 22.2 18.5 24.1 18.3 17.8 19.5 R035 119420 17.3 17.6 14.1 13.2 16.3 15.3 R036 120604 10.2 12.3 13.2 13.2 13.4 12.4 R037 121353 9.5 10.9 12.2 12.2 12.4 12.3 R038 121681 18.3 18.4 15.1 15.2 14.2 17.5 R039 122382 17.3 18.3 14.3 13.6 19.7 18.5 R040 31707 11.4 10.8 10.9 10.3 10.3 10.5 R041 52056 12.1 12.2 11.3 9.2 9.3 7.5 R042 70758 14.3 15.2 14.1 14.8 15.6 13.7 R043 89270 20.6 21.1 14.6 13.2 14.3 13.2 R044 89446 5.2 3.4 5.7 4.2 4.3 6 R045 90496 15.3 16.2 10.4 15.6 22.2 23.3 R046 98645 12.3 12.3 13.5 11.5 15.2 13.7 R047 100548 17.2 17.2 15.2 17.4 15.8 14.5 R048 101256 8.4 10.5 12.4 10.9 11.5 5.7 R049 102366 7 6.8 5.7 6.8 8.5 6.8 R050 109381 16.5 16.5 14.2 14.6 17.4 16.6 R051 111277 15.3 16.2 14.2 16.4 20.5 17.7 R052 112825 11.3 12.5 12.1 14.3 15.4 16.2 R053 117847 21.3 17.8 15.4 12.4 15.3 15.6 R054 121047 16.1 18.7 13.4 15.2 19.1 17.6 R055 122193 17.4 17.2 14.2 16.2 16.6 19.8 R056 122805 17.7 17.3 15.1 12.6 17.2 17.9 66 Table 8: Measured distance (in mm) corresponding to each location in the AI software (Diagnocat) in the retrospective study (first reading). Diagnocat First reading Research Nr. Assigned number RM2 RM1 RP2 LP2 LM1 ML2 Incidental findings R001 33761 21.2 22.8 15.7 15.3 20.1 20 R002 70758 13.6 16 17 17.6 16.7 13.4 Yes R003 71318 19.1 20.9 15.5 19.9 17.8 19.2 R004 78722 16.7 10.6 10 12.8 8.5 16.6 R005 99972 16.1 17.7 19.2 17.7 15.4 15.1 R006 101250 11.3 11 9.7 10.8 11.7 11.7 Yes R007 106525 11.2 11.6 12.7 16.5 12 12.2 Yes R008 107751 15.1 15.5 13.2 15.6 19.9 18.9 R009 110879 19.6 22.2 17.5 15.2 21.3 20.5 R010 114531 9 9 10 6.6 9.1 9.8 R011 114562 14.9 16.7 17.4 17 14.4 13.4 R012 114629 15.4 17.5 15.3 17.9 19.4 16 Yes R013 116629 12.8 14.2 13.2 11.5 16.2 13.6 R014 119284 16.2 15.5 20.8 17.7 15.5 18.8 Yes R015 119873 Excluded R016 120187 14.9 15.9 13.4 14 16.7 15.1 R017 120606 11.6 13.3 13.1 12.4 14.1 14.2 R018 121708 15.2 15.2 13.8 14.8 17.6 13.9 R019 122247 16.2 17.1 18.7 15.4 12.8 11.6 R020 122814 11.9 12 17 12.9 14.8 13.1 Yes R021 122897 19.5 23.1 13.3 13.7 25.3 23.8 R022 47791 Excluded R023 72860 Excluded R024 75109 Excluded R025 77535 Excluded R026 81192 15.8 20.6 14.6 18.5 17 16.3 R027 109848 14.7 16 14.5 10.5 11.1 11.6 R028 110879 18.4 22.8 17.1 17.7 21.2 20.5 R029 113559 17.6 19.3 14.6 15 18.4 16.7 R030 114246 13.2 16.6 12.5 12 16.5 17 67 R031 114248 14.5 15.6 11.5 12 16.2 13.6 R032 118059 13.6 15.5 12.4 11 17.6 16 R033 119190 14.4 15.3 13.2 11.7 12.7 11.4 R034 119324 19.6 17.9 22.8 19.3 18.6 18.3 Yes R035 119420 15.4 17.9 13.3 14.8 16.9 14.3 R036 120604 Excluded reading low position showing vertebrae R037 121353 Excluded reading 10x10 edentul R038 121681 16 18.8 12.7 14.6 16.1 14.6 R039 122382 Excluded reading low position showing vertebrae R040 31707 11.7 10.9 10.1 9.9 9.4 10.3 Yes R041 52056 12 12.4 11.2 9 9.4 7.8 R042 70758 12.2 18.7 13.8 11.6 14.9 12.6 R043 89270 19 21.1 14.8 13.3 14.7 11.3 Yes R044 89446 Excluded tracing metal frame R045 90496 14.9 16.6 11.7 15.8 23.8 20.4 Yes R046 98645 13.1 16.3 13.8 12.2 14.2 14 R047 100548 18.6 18.4 14.6 16 18.6 16.5 R048 101256 8.9 12.1 11.3 10.2 10.3 5.8 R049 102366 5.7 6.5 5.6 6 6.6 6.5 R050 109381 15.8 17.6 11.8 11.8 17 17.2 Yes R051 111277 15.4 16.4 13.8 14.8 18.8 16.8 R052 112825 10.5 12.7 13.1 13 15.5 15.8 R053 117847 19.1 19.6 14 11.4 14.1 15.1 Yes R054 121047 14.3 16.5 14.2 11.3 18.5 16.4 R055 122193 16.9 16.8 12.6 12.6 16.7 17.8 R056 122805 17.1 16.6 16.6 13.8 17.6 15.8 Table 9: Measured distance (in mm) corresponding to each location in the AI software (Diagnocat) in the retrospective study (second reading). Diagnocat Second reading Research Nr. Assigned number RM2 RM1 RP2 LP2 LM1 ML2 R001 33761 20.6 23 15.6 15.7 20.1 19.7 R002 70758 13.5 16 17.2 17.8 16.9 13.9 R003 71318 20.3 20.4 15.5 19.3 17.3 19.7 R004 78722 16.4 10.3 10.3 12.7 8.6 16.8 R005 99972 16.1 17.2 19.6 18.3 15.3 16.7 68 R006 101250 11.3 11 9.4 10.8 11.4 11.8 R007 106525 11.4 11.7 12.2 16.3 12.3 12.1 R008 107751 15.2 15.4 13.6 15.7 19.6 18.6 R009 110879 19.4 22.1 17.3 15.7 21.4 20.3 R010 114531 10 9.3 9.9 6.5 9 10.6 R011 114562 14.7 16.5 17.3 17.2 14.2 13.1 R012 114629 15.3 17.4 15.3 17.8 19.3 16.2 R013 116629 12.3 14.2 13.5 11.4 16.8 13.5 R014 119284 16.2 15.6 19.8 18.3 15.6 18.5 R015 119873 Excluded R016 120187 15.2 15.4 13.2 14.6 16.5 15.3 R017 120606 11.5 13.6 13.6 12.7 14.3 14.2 R018 121708 15.1 15.6 13.6 14.3 17.7 13.8 R019 122247 16.3 17.3 18.6 15.3 12.7 11.5 R020 122814 12.2 12.1 16.4 12.7 15 13.7 R021 122897 19.4 23.2 13.2 13.6 25.1 23.4 R022 47791 Excluded R023 72860 Excluded R024 75109 Excluded R025 77535 Excluded R026 81192 15.8 20.6 14.6 18.5 17 16.3 R027 109848 14.6 16.2 14.3 10.4 11.3 11.4 R028 110879 18.3 22.5 17.3 17.6 21.4 20.4 R029 113559 17.4 19.4 14.6 15.2 18.3 16.8 R030 114246 13.2 16.6 12.5 12 16.5 17 R031 114248 14.5 15.7 11.4 12.3 16.3 13.4 R032 118059 13.3 15.7 12.3 11.6 17.4 15.6 R033 119190 14.5 15.2 13.1 11.6 12.6 11.3 R034 119324 19.5 17.9 22.7 19.4 18.5 18.4 R035 119420 15.3 17.5 13.5 14.7 16.8 14.2 R036 120604 Excluded R037 121353 Excluded R038 121681 16.1 18.8 12.7 14.5 16.1 14.6 R039 122382 Excluded R040 31707 11.6 10.8 10.2 9.7 9.3 10.2 R041 52056 12 12.4 11.2 9 9.4 7.8 R042 70758 12.3 18.6 13.5 11.5 14.8 12.5 R043 89270 18.7 21.2 13.8 12.7 14.5 11.6 R044 89446 Excluded 69 R045 90496 14.5 16.1 11.7 15.2 23.4 20.2 R046 98645 13.2 16.5 13.4 12.3 14.1 14.2 R047 100548 18.4 18.3 14.5 16.2 18.4 16.7 R048 101256 8.8 12.2 11.2 10.1 10.2 5.9 R049 102366 5.8 6.6 5.4 6.4 6.5 6.6 R050 109381 15.7 16.8 11.5 12.4 17.2 16.9 R051 111277 15.3 16.2 13.7 14.5 18.9 16.9 R052 112825 10.2 12.9 12.7 13.5 15.8 15.9 R053 117847 19.3 19.3 14.2 11.5 14.3 15.3 R054 121047 14.3 16.5 14.2 11.2 18.5 16.3 R055 122193 16.9 16.3 12.8 12.2 16.3 17.2 R056 122805 17 17.1 16.2 12.4 17.9 15.8 70 Appendix B Calculation of ICC: We followed intra-examiner and inter-examiner reliability protocol in the retrospective study to reduce bias. A randomization of the study sample was performed using Microsoft word software and Dr. David MacDonald reviewed my measurements for each case to check for accuracy and the agreement between us on the measurements were accurate as 0.98 and 0.99 for intra-examiner reliability. Table 10: Randomization of the study sample in the retrospective study using Microsoft word software. Research Nr. Randomized Assigned number R015 0.791113467 119873 R011 0.867921186 114562 R003 0.225740829 71318 R018 0.534905756 121708 R017 0.909737209 120606 R029 0.862935556 113559 R032 0.413681889 118059 R038 0.578098919 121681 R027 0.306023527 109848 R025 0.166898609 77535 R008 0.867269773 107751 R010 0.184430198 114531 R052 0.14984352 112825 R014 0.771568591 119284 R036 0.458859952 120604 R024 0.458753717 75109 R054 0.195940245 121047 R034 0.227363897 119324 R053 0.298435571 117847 71 R021 0.163543462 122897 R040 0.267982372 31707 R020 0.309274823 122814 R049 0.887213939 102366 R043 0.147017918 89270 R055 0.930928741 122193 R022 0.072357951 47791 R046 0.158480592 98645 R007 0.35973303 106525 R016 0.522909923 120187 R056 0.78482921 122805 R012 0.512615224 114629 R035 0.648873636 119420 R041 0.198253258 52056 R005 0.338122315 99972 R050 0.376489851 109381 R039 0.486600226 122382 R030 0.954910101 114246 R023 0.115396714 72860 R019 0.018765945 122247 R002 0.063692844 70758 R006 0.94134246 101250 R051 0.845047583 111277 R042 0.364284097 70758 R013 0.458548543 116629 R044 0.715334072 89446 R001 0.254922214 33761 R048 0.912946208 101256 R047 0.483121638 100548 R004 0.496535537 78722 R026 0.204356873 81192 R009 0.317030068 110879 R037 0.512704171 121353 R028 0.404560732 110879 R045 0.032422532 90496 R033 0.602451437 119190 R031 0.165856631 114248 72 Appendix C Table 11: Description of the total sample in the retrospective study. Table 12: Independent samples t-test for total edentulous and dentate regions compared to AI and Manual tracing in the retrospective study. 73 Table 13: Two tailed Independent t-test for total edentulous and dentate regions compared to AI and Manual tracing in the retrospective study.
Thesis/Dissertation
eng
Craniofacial Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
MacDonald, David, 1955-
Pre-implant tracing of mandibular canals on CBCT images using artificial intelligence
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