Toward Precision Diagnosis of Maxillofacial Pathologies by Artificial Intelligence Algorithms: A Systematic Review.

Abstract

Purpose: This review highlights the potential of artificial intelligence algorithms, including machine learning (ML) and deep learning (DL), in improving the diagnosis and management of oral and maxillofacial diseases through advanced imaging techniques such as computerized tomography (CT) and cone-beam computed tomography (CBCT).

Methods: The current review was conducted on the basis of ISI Web of Science, PubMed, Scopus, and Google Scholar (2010-2024) using keywords related to radiography, MRI, CT, CBCT, ML, DL, and maxillofacial pathology, with a focus on clinical applications.

Results: The DL algorithms for detecting vertical root fractures achieved a diagnostic accuracy of 89.0% for premolars, with a sensitivity of 84.0% and specificity of 94.0%. It demonstrated an accuracy of 93% and a specificity of 88% in evaluating CBCT images. The GoogLeNet Inception v3 architecture achieved an AUC of 0.914, sensitivity of 96.1%, and specificity of 77.1% for CBCT, outperforming the panoramic radiograph, which had an AUC of 0.847, sensitivity of 88.2%, and specificity of 77.0%. CBCT demonstrated higher diagnostic accuracy (91.4%) than panoramic images (84.6%), with odontogenic cystic lesions exhibiting the highest accuracy. The U-Net-based DL algorithm achieves recall, precision, and F1 scores of 0.742, 0.942, and 0.831 for metastatic lymph nodes, and 0.782, 0.990, and 0.874 for nonmetastatic lymph nodes, respectively.

Conclusion: This study highlights the superior anatomical detail of CBCT, making it more reliable for diagnosing oral and dentomaxillofacial disorders. DL algorithms demonstrate high accuracy and sensitivity in diagnosing dental and odontogenic disorders and often outperform radiologists.