S471 Artificial Intelligence in Endoanal Ultrasound: A Multicentric Study on a Multilesion Model for Benign Anorectal Lesion Detection and Differentiation

Introduction: Endoanal Ultrasound (EAUS) is a key diagnostic tool for anorectal diseases, valued for its simplicity and tolerability. However, its interpretation is challenged by significant interobserver variability, a steep learning curve, and limited accessibility. The application of artificial intelligence (AI) to enhance EAUS remains in its early stages. The aim of this study was to develop and validate an interoperable AI model capable of differentiating multiple benign anorectal lesions using EAUS. Methods: An EfficientNet-based AI model was developed using 4,722 EAUS frames, from 105 procedures performed across 3 centers using 2 different ultrasound devices. Two experts independently classified frames into 5 categories: external anal sphincter (EAS) laceration, internal anal sphincter (IAS) laceration, anal fissure (AF), intersphincteric fistula (ISF), and transsphincteric fistula (TSF). Frames with concordant classifications established the ground truth. The dataset was split into training/validation (90%, with 3-fold cross-validation) and test (10%) sets. The model with the best F1-score was tested Performance metrics included accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Results: Training/validation mean (±SD) accuracies were: EAS 0.85 (±0.03), IAS 0.85 (±0.003), AF 0.98 (±0.02), ISF 0.99 (±0.01), TSF 0.99 (±0.01). On the test set, respective accuracies were: EAS 0.88, IAS 0.88, AF 0.99, ISF 1.00, TSF 0.99. Conclusion: This pioneering AI model demonstrates high accuracy in differentiating 5 benign anorectal lesions using EAUS. As the first multilesion solution in this area, it represents a significant step toward standardizing EAUS interpretation and expanding access to expert-level diagnostic support. With further prospective validation, this technology has the potential to transform clinical workflows and improve decision-making in anorectal care.