Artificial Intelligence Applications in Endodontic Diagnosis and Treatment Planning
Keywords:
Artificial intelligence, endodontics, diagnosis, CBCT, deep learning, treatment planningAbstract
Endodontic diagnosis and treatment planning, which is the focus of artificial intelligence (AI), has improved the accuracy, efficiency, and predictability of root canal procedures. The artificial intelligence (AI) algorithms, especially, the deep learning models, such as the convolutional neural networks (CNNs), can be used to examine radiographic images, including periapical X-rays and cone-beam computed tomography (CBCT) pictures, to identify small pathologies, which are not easily detected by human specialists. AI is sensitive and specific in diagnosis of periapical lesions, pulp vitality (reversible/ irrevocable pulpitis, necrosis) and root fractures, with a sensitivity and specificity of up to 85- 90 in controlled studies. The systems automatically divide root canal anatomy and identify accessory canals, C-shaped shapes or calcifications in the molars, and this minimizes diagnostic mistakes and missed canals during therapy. In treatment planning, AI combines clinical information, radiographs, and patient history to estimate the level of complexity and success of the case and postoperative pain. Machine learning models predict the optimal file sequences, irrigation regulations, and obturation strategies, whereas the prognostic tools predict the risk of retreatment or the necessity of surgical referral. This evidence-based method will reduce the number of iatrogenic errors, reduce the time spent on the procedure, and enhance the results with complex anatomies, such as fused roots or retreatment cases. Although such improvements exist, AI is still a supplementary tool, subject to the limitations of dataset biases, the necessity to prove their usefulness in a variety of populations, and regulatory issues. The prospects of integration into clinical workflows are individualized endodontics, but its ethical implementation is ensured by the control of clinicians. Future opportunities are real-time AI-based guidance throughout the procedures and multimodal AI fusion CBCT-intraoral scans.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Odontia : Journal of Dental Innovations and Oral Health

This work is licensed under a Creative Commons Attribution 4.0 International License.