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Updated: Jan 11, 2026
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
Diagnostic Accuracy of Artificial Intelligence in Detecting Oral Potentially Malignant Disorders and Oral Cancer: A
M Nayana1, J Avinash1, M Y Jayachandra2
1Department of Public Health Dentistry, Dayananda Sagar College of Dental Sciences, Bengaluru, Karnataka, India.
Aim:
Oral cancer is a leading cause of cancer-related morbidity, especially in low-resource settings. Early detection of oral potentially malignant disorders (OPMDs) is key to reducing disease burden. This study was carried out to evaluate the diagnostic accuracy of AI-assisted imaging tools in the detection of OPMDs and oral cancer in community and clinical settings.
Materials And Methods:
We searched PubMed, IEEE Xplore, Scopus, and Web of Science (2015-2024) for studies using AI to detect OPMDs or oral cancer from images, reporting sensitivity and specificity. A random-effects meta-analysis was conducted. Quality was assessed using QUADAS-2. Summary receiver operating characteristic curves were generated to evaluate the global performance.
Results:
From 1092 articles screened, 35 studies met inclusion criteria. These studies evaluated >15,000 images using clinical photography, histopathology, optical coherence tomography, and autofluorescence. Pooled sensitivity was 0.919% (95% CI: 0.89-0.94), specificity 0.879 (95% CI: 0.84-0.91), area under curve 0.9758, and diagnostic odds ratio 131.63. Deep learning methods-particularly convolutional neural networks-consistently demonstrated superior performance.
Conclusion:
AI-assisted diagnostic systems demonstrate high accuracy and potential for scalable, non-invasive screening of OPMDs and oral cancer. Integration into public health programs, particularly in underserved settings, could significantly improve early detection outcomes. Mobile-compatible platforms represent a viable public health tool for oral cancer control.
