Pre-implant tracing of mandibular canals on CBCT images using artificial intelligence
department or school: Dentistry, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Dadoush, Maher
issued: 2024-06-30T07:00:00Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: 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 i…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/85371
note: 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 bilaterall…
has type: Thesis/Dissertation
date issued: 2023-11
is shown at: 10.14288/1.0434634
language: eng
degree discipline: Craniofacial Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: MacDonald, David, 1955-
type: Text
identifier URI: http://hdl.handle.net/2429/85371