Artificial Intelligence in the study of root and canal anatomy: a comprehensive review on applications, advantages, challenges and future directions

Ahmed, Hany Mohamed Aly and Al-Maswary, Arwa and Habaebi, Mohamed Hadi and Tasdelen, Abdulkadir and Al Husaini, Mohammed Abdulla Salim and Elnawawy, Hoda Mohamed Abdelrazek and Buzayan, Muaiyed Mahmoud Ali and Yahya, Noor Azlin and ElKezza, Aeman and Ahmed, Hithem and Dumner, Paul Michael Howell (2025) Artificial Intelligence in the study of root and canal anatomy: a comprehensive review on applications, advantages, challenges and future directions. European Endodontic Journal, 10 (5). pp. 343-364. E-ISSN 2548-0839

PDF - Submitted Version Restricted to Registered users only Download (3MB) | Request a copy

Abstract

two decades, technological advances in 3D imaging have revealed the complexities of root and canal anatomy. Recently, artificial intelligence (AI) models have been developed and are being applied to a range of fields within medicine and dentistry. There is an emerging trend for the application of this technology in 2D and 3D imaging tools to study the anatomical features of roots and canals. This narrative review provides a comprehensive analysis of AI applications in the study of root and canal anatomy and their implications for education, research and clinical practice. The analysis reveals that AI applications for the study and teaching of root and canal anatomy are promising; however, more investigations are warranted with larger datasets to provide more accurate deep learning models. Students, researchers and clinicians should understand the inherent limitations of AI data generated from 2D and 3D imaging devices. Future studies are needed to assess what effect deep learning models have on the diagnostic and operative clinical skills of students and dental practitioners when managing teeth with different levels of anatomical complexities.

Item Type:	Article (Note)
Uncontrolled Keywords:	Artificial Intelligence, deep learning, dental pulp cavity, machine learning, neural networks, root and canal anatomy
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	24 Sep 2025 16:10
Last Modified:	24 Sep 2025 16:10
URI:	http://irep.iium.edu.my/id/eprint/123275

Actions (login required)

View Item