Title
Machine learning prediction of canal transportation using micro-CT data
Authors
Ekta Chaudhari1, Arun Kumar Dagur2, Meetkumar Dedania3, Rupali Baban Wetam4,*, Vipin Arora5 & Sourav Sen6
Affiliation
1Department of Conservative Dentistry and Endodontics, Siddhpur Dental College, Sabarkantha, Gujarat, India; 2Department of Dentistry, KM Medical College, Mathura, Uttar Pradesh, India; 3Department of Conservative Dentistry and Endodontics, K. M. Shah Dental College and Hospital, Sumandeep Vidyapeeth Deemed to be University, Piparia, Vadodara, Gujarat, India; 4Department of Conservative Dentistry and Endodontics at Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Sangli, India; 5Department of Restorative Dental Sciences, Taif University, Saudi Arabia; 6Department of Public Health Dentistry, Maharishi Markandeshwar College of Dental Sciences & Research, Mullana, Ambala, Haryana, India; *Corresponding author
Ekta Chaudhari - E-mail: Ekta878@gmail.com
Arun Kumar Dagur - E-mail: akdagur90@gmail.com
Meetkumar Dedania - E-mail: meet97247da@gmail.com
Rupali Baban Wetam - E-mail: rupaliwetam8@gmail.com
Vipin Arora - E-mail: vipinendodontist@gmail.com
Sourav Sen - E-mail: drsouravsen@gmail.com
Article Type
Research Article
Date
Received April 1, 2026; Revised April 30, 2026; Accepted April 30,
2026, Published April 30, 2026
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.
Keywords
Machine learning (ML), canal transportation, micro-CT, root canal morphology, endodontics
Citation
Chaudhari et al. Bioinformation 22(4): 2565-2571 (2026)
Edited by
Hiroj Bagde
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
