S572 Deep Learning and High Resolution Anoscopy: First Trinary Interoperable Model for Detecting and Differentiating Clinically Relevant Anal Lesions

Introduction: High-Resolution Anoscopy (HRA) is the gold standard for anal cancer screening. Early detection of premalignant lesions, such as high-grade squamous intraepithelial lesions (HSIL), is essential for reducing anal cancer mortality. However, differentiating HSIL from other human papilloma virus-related lesions—such as low-grade squamous intraepithelial lesions (LSIL) and non-dysplastic mimickers (e.g., inflammation)—remains suboptimal, even among experienced clinicians. This study aimed to develop the first Artificial Intelligence (AI) model capable of detecting and differentiating anal canal lesions across these 3 categories. Methods: A multicenter, interoperable deep learning model was developed across 5 clinical sites using a YOLOv11-based object detection architecture using bounding boxes. The model was trained on 191,951 frames extracted from 107 High-Resolution Anoscopy (HRA) procedures. Each frame was annotated based on corresponding histopathological analysis. The dataset was divided into training, validation, and testing subsets. Lesion detection was defined by accurate placement of bounding boxes. The model’s recall (sensitivity), precision (positive predictive value), and accuracy were calculated as weighted averages across 5 confidence thresholds (0.45, 0.47, 0.50, 0.52, 0.55). Results: Overall, the accuracy of the model was 95.2% (94.7-95.7). For HSIL, recall and precision were 97.9% (97.6–98.2) and 93.9% (92.8–95.1), respectively. For LSIL, recall and precision were 98.9% (98.8–99.0) and 98.5% (98.3–98.7), respectively. For non-dysplastic lesions, recall and precision were 98.7% (98.5–98.8) and 96.0% (94.9–97.2), respectively. Conclusion: This pioneering trinary classification model, capable of accurately distinguishing HSIL, LSIL, and non-dysplastic mimickers, marks a significant breakthrough in the path toward clinical integration of AI-assisted HRA. By enabling precise and consistent identification of clinically relevant anal lesions, AI may enhance diagnostic accuracy, reduce interobserver variability, and pave the way for more targeted, precision-based screening and management strategies.