Machine learning prediction of canal transportation using micro-CT data.
Authors
Affiliations (6)
Affiliations (6)
- Department of Conservative Dentistry and Endodontics, Siddhpur Dental College, Sabarkantha, Gujarat, India.
- Department of Dentistry, KM Medical College, Mathura, Uttar Pradesh, India.
- Department of Conservative Dentistry and Endodontics, K. M. Shah Dental College and Hospital, Sumandeep Vidyapeeth Deemed to be University, Piparia, Vadodara, Gujarat, India.
- Department of Conservative Dentistry and Endodontics at Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Sangli, India.
- Department of Restorative Dental Sciences, Taif University, Saudi Arabia.
- Department of Public Health Dentistry, Maharishi Markandeshwar College of Dental Sciences & Research, Mullana, Ambala, Haryana, India.
Abstract
Root canal transportation remains a significant complication in endodontic treatment because current assessment methods cannot predict transportation risk prior to instrumentation. Therefore, it is of interest to develop and validate machine learning models to predict the magnitude and direction of canal transportation using pre-operative micro-CT-derived morphometric features. Hence, a total of 120 mandibular molars with moderate-to-severe canal curvature were scanned pre- and post-instrumentation and seventeen morphometric variables were used to train four machine learning algorithms with five-fold cross-validation. The gradient boosting model demonstrated the best performance, with a coefficient of determination of 0.87, mean absolute error of 0.031 mm and root mean square error of 0.042 mm in predicting apical transportation. Thus, machine learning models based on pre-operative micro-CT data can accurately predict canal transportation and may aid in risk assessment and selection of optimal instrumentation strategies in endodontic practice.