口腔病変の正確な検出と分類のための人工知能ベースの診断ツールの評価：臨床研究

Early detection and accurate classification of oral lesions are essential for effective clinical management and prevention of malignant transformation. Recent advancements in artificial intelligence (AI) have led to the development of diagnostic tools aimed at enhancing clinical decision-making.

A prospective clinical study was conducted involving 180 patients presenting with suspicious oral lesions at a tertiary dental care center. Digital images of lesions were captured and analyzed using an AI-powered diagnostic software trained on a dataset of over 10,000 labeled images. Each case was also evaluated by two oral pathologists, followed by histopathological confirmation. Sensitivity, specificity, accuracy, and Cohen's kappa coefficient were calculated to assess diagnostic concordance.

The AI system achieved an overall diagnostic accuracy of 91.1% (±4.3), sensitivity of 93.6%, and specificity of 89.2%. The concordance between AI and histopathological diagnosis was substantial (κ =0.83; P < 0.001). The highest performance was observed in detecting leukoplakia (95.2% accuracy) and oral lichen planus (92.4%). Misclassification occurred primarily in cases of mild dysplasia and nonspecific ulcerations.

The AI-based diagnostic tool demonstrated high diagnostic accuracy and substantial agreement with histopathological findings. It shows promise as a chairside adjunct to enhance clinical decision-making and early detection of potentially malignant oral lesions.