Real-Time Application of Artificial Intelligence for Automatic Detection of High-Grade Squamous Intraepithelial Lesions During High-Resolution Anoscopy

Background: High-resolution anoscopy (HRA) is the gold standard for anal cancer screening, but its interpretation is challenging and operator-dependent. Artificial intelligence (AI) may increase diagnostic yield, but most studies have focused on differentiating low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL, respectively) in still frames, with no clinical application reported. Methods: We describe the first real-time use of deep learning, demonstrated in three patients undergoing HRA for anal cancer screening, at a high-volume American referral center. When an area suggestive of HSIL appeared, a YOLO-based object detection model generated a bounding box. Results: The model detected only histologically confirmed HSIL and did not activate when no lesions or only LSIL were present. Conclusions: This report suggests that real-time AI-enhanced HRA is feasible and may improve lesion detection and differentiation, potentially representing a significant step forward in this demanding field, although multicentric validation studies are still needed.