THE APPLICATION OF ARTIFICIAL INTELLIGENCE FOR PERIAPICAL LESIONS IDENTIFICATION
DOI:
https://doi.org/10.70614/fagrz313Keywords:
artificial intelligence, endodontics, dental radiographic imagingAbstract
Periapical lesions are among the most common dental pathologies and are radiographically characterized by periapical radiolucency; however, they may be underdiagnosed in asymptomatic patients. The aim of this study is to evaluate the diagnostic accuracy of artificial intelligence in detecting these apical pathologies in panoramic radiographs, periapical radiographs, and cone-beam computed tomography scans. Convolutional neural networks (CNNs) have demonstrated high accuracy, precision, and sensitivity, contributing to improved diagnostic performance and faster interpretation by dental practitioners. These deep learning tools are transforming dentistry and may support clinicians and the oral healthcare system. Although further improvements are required to enhance their robustness, their future application appears promising.
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