AI-assisted real-time detection of canal morphology during endodontic treatment using an operating microscope

Abstract

The integration of artificial intelligence (AI) with operating microscopes represents a significant
advancement in modern endodontics by enhancing the real-time detection of complex root canal
morphology during treatment. Accurate identification of root canal anatomy is essential for successful
endodontic therapy, as undetected canals and anatomical variations remain major causes of treatment
failure. Conventional diagnostic approaches, including periapical radiography, cone-beam computed
tomography, and magnification through operating microscopes, have substantially improved clinical
outcomes but continue to rely heavily on the clinician’s expertise and interpretation. AI-assisted image
analysis introduces automated recognition of canal structures through advanced machine learning and
deep learning algorithms capable of processing microscopic images in real time. These technologies
can improve the detection of accessory canals, calcified canals, canal bifurcations, and other anatomical
complexities while providing immediate clinical guidance during instrumentation. The integration of
AI with digital imaging workflows has the potential to increase diagnostic accuracy, reduce procedural
errors, improve treatment efficiency, and support minimally invasive endodontic procedures. Despite
challenges related to algorithm validation, data standardization, equipment integration, and regulatory
considerations, ongoing technological advancements continue to expand the clinical applicability of
AI-assisted systems. The incorporation of intelligent image analysis into operating microscopy is expected
to transform endodontic practice by improving precision, consistency, and long-term treatment outcomes.