Farihah Septina, Bramma Kiswanjaya
Introduction: Early detection of periapical lesions is critical for timely clinical intervention. Artificial Intelligence (AI) technology, particularly deep learning models, offers a promising approach for identifying such lesions. This study systematically reviews the application of deep learning in detecting periapical lesions across three radiographic modalities: periapical radiography, panoramic radiography, and Cone-Beam Computed Tomography (CBCT). Methods: This study employs a systematic literature review methodology, adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure methodological rigor and transparency. Results: The results of this study demonstrate that integrating AI with periapical, panoramic, and CBCT enhances diagnostic accuracy and efficiency in the detection of periapical lesions. Discussion: The integration of artificial intelligence with panoramic, periapical, and CBCT imaging modalities significantly enhances the diagnostic accuracy and operational efficiency in identifying periapical lesions. Conclusion: This study found that AI performs better at evaluating mandibular periapical lesions than at evaluating maxillary periapical lesions. Furthermore, periapical radiography is more sensitive than panoramic radiography for detecting smaller periapical lesions, whereas CBCT provides the highest diagnostic accuracy for both periapical and odontogenic cystic lesions. The integration of AI with radiographic technologies shows significant potential to enhance diagnostic precision, optimize treatment planning, and improve patient outcomes in endodontic practice. © 2026 The Author(s). Published by Bentham Open., 2026. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Department of Dental Radiology, Universitas Indonesia, Jakarta, Indonesia; Department of Dental Radiology, Universitas Brawijaya, Malang, Indonesia
