Artificial Intelligence for Anal Cancer Screening: Multicenter Multicontinental Validation of a Trinary Model in High-Resolution Anoscopy.

Background and Aims: Anal cancer incidence is rising, primarily due to increasing rates of human papillomavirus infection. High-resolution anoscopy (HRA) is the gold standard diagnostic modality for evaluating human papillomavirus-related anal lesions; however, its broader adoption is limited by subjectivity and interobserver variability. This study aims to develop and validate a deep learning-based trinary classification model that detects and classifies high-grade squamous intraepithelial lesion (HSIL), low-grade squamous intraepithelial lesion (LSIL), and non-dysplastic lesions (eg, inflammation) on HRA frames. Methods: A multicentric study was conducted to develop a convolutional neural network for automatic detection and classification of HSIL, LSIL, and non-dysplastic lesions. A You Only Look Once (YOLO)-v11 model was trained and validated on 191,951 frames (from 107 HRA procedures) containing histologically confirmed lesions, collected from 5 different imaging devices across 5 independent centers. Performance metrics (recall, precision, accuracy, and F1-score) were calculated at the object classification level as weighted averages across 5 confidence thresholds (0.45, 0.47, 0.50, 0.52, and 0.55). Results: For HSIL and LSIL, recall was 97.9% and 98.9%, and precision was 93.9% and 98.5%, respectively. For non-dysplastic lesions, recall was 98.7%, and precision was 96.0%. The overall classification accuracy on the test set was 88.1% (95% CI: 78.2-98.1). Conclusion: This is the first interoperable artificial intelligence model capable of simultaneous detection and trinary classification of anal canal lesions (HSIL, LSIL, and non-dysplastic mimickers). By addressing key limitations of HRA, this model supports broader clinical applicability and represents a significant step toward real-world deployment and personalized artificial intelligence-assisted care.