Artificial Intelligence-Assisted reflectance confocal microscopy for Real-Time intraoperative margin assessment in oral squamous cell carcinoma.

BACKGROUND: Oral cavity squamous cell carcinoma (OSCC) is a global health burden, where negative margins are essential for reducing recurrence and improving survival. Intraoperative frozen-section analysis is limited by time, sampling error, and interpretive variability, underscoring the need for more reliable margin assessment. Reflectance confocal microscopy (RCM) enables real-time, in vivo high-resolution imaging, but accuracy depends on expert interpretation. This study evaluated the diagnostic performance of an artificial intelligence (AI)-driven model for RCM in OSCC, aiming to develop a point-of-care platform for intraoperative use. METHODS: Patients with biopsy-confirmed OSCC underwent in vivo RCM imaging using a handheld intraoral probe before biopsy. Histopathology was the reference standard. A deep learning model was developed with the Google Cloud Vertex AI Automated Machine Learning (AutoML) Vision platform and trained on 4,090 annotated RCM images (1,998 benign, 2,092 malignant). Performance was compared with blinded expert pathologist and RCM readers. RESULTS: The AI model achieved an area under the precision-recall curve (AUC-PR) of 0.99 and an area under the receiver operating characteristic curve (AUC-ROC) of 0.99, with sensitivity 98.09%, specificity 95.00%, accuracy 96.58%, positive predictive value (PPV) 95.35%, and negative predictive value (NPV) 97.94%. Expert readers showed sensitivity 90.00%, specificity 98.30%, accuracy 94.15%, PPV 88.20%, and NPV 96.60%. Inter-reader agreement was 95.00% for benign and 81.70% for malignant cases. CONCLUSIONS: AI-driven RCM interpretation provides an accurate, rapid, noninvasive approach for OSCC diagnosis and intraoperative margin assessment. It outperformed expert readers and can reduce reliance on frozen-section analysis, streamline workflows, and improve outcomes.