Applications and clinical translation of artificial intelligence in CBCT-based detection of endodontic lesions: a scoping review.
Authors
Affiliations (3)
Affiliations (3)
- Department of Conservative Dentistry and Endodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 602105, Tamil Nadu, India. [email protected].
- Department of Dental Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 602105, Tamil Nadu, India.
- Department of Dental Research Cell, Dr. D. Y. Patil Dental College & Hospital, Dr. D. Y. Patil Vidyapeeth (Deemed to be University), Pimpri, 411018, Pune, India.
Abstract
Artificial intelligence (AI) especially deep learning (DL) has significantly revolutionized medical image analysis, which include dental diagnostics. In endodontics, the combination of AI and CBCT provides automatic detection, classification, and segmentation of periapical lesions. This scoping review used the Arksey and O'Malley model, as updated by Levac et al., and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. An extensive search strategy was developed to identify pertinent studies in various electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar. The search was conducted for 2010-2025 articles. Studies were included if they applied AI models to CBCT imaging for detecting, classifying, or segmenting periapical lesions. Data were charted on model types, imaging approaches, diagnostic metrics, and clinical translation. AI models used were CNN-based models including U-Net, DenseNet, custom architectures such as PALNet, and commercial packages such as Diagnocat. High reported diagnostic performance was sensitivity of 67.3% to 97.1% and AUC of up to 0.98. While several studies demonstrated high diagnostic accuracy and potential for clinical decision support, the majority were retrospective, used small or homogenous datasets, and lacked external validation or standard ground truth comparisons (e.g., histological correlation). AI demonstrates promising potential in enhancement of the diagnostic accuracy and efficiency of CBCT-based periapical lesion assessment. DL models such as U-Net, PAL-Net, and commercial software as of Diagnocat, have shown good performance on the tasks of segmentation and classification. However, additional prospective validation, model calibration, and real-world clinical use, studies are necessary to establish reliability and generalizability. The evidence from the current review leans towards an increasing body of evidence in support of AI use in CBCT-based periapical lesion detection. Further, reported the clinical potential of AI to decrease diagnostic time, increase consistency, and assist less experienced operators.