Artificial intelligence and endoanal ultrasound: pioneering automated differentiation of benign anal and sphincter lesions

TL;DR

This paper introduces an AI tool that can accurately distinguish between different types of benign anal injuries using ultrasound images, potentially improving diagnosis and reducing reliance on expert interpretation.

Contribution

The paper presents the first AI model for automated differentiation of benign anal and sphincter lesions using endoanal ultrasound.

Findings

The CNN achieved 100% sensitivity, specificity, and accuracy for anal fissures.

For external lacerations, the model achieved 82.5% sensitivity, 93.5% specificity, and 88.2% accuracy.

The model demonstrated 91.7% sensitivity and 85.9% specificity for internal lacerations.

Abstract

Anal injuries, such as lacerations and fissures, are challenging to diagnose because of their anatomical complexity. Endoanal ultrasound (EAUS) has proven to be a reliable tool for detailed visualization of anal structures but relies on expert interpretation. Artificial intelligence (AI) may offer a solution for more accurate and consistent diagnoses. This study aims to develop and test a convolutional neural network (CNN)-based algorithm for automatic classification of fissures and anal lacerations (internal and external) on EUAS.

A single-center retrospective study analyzed 238 EUAS radial probe exams (April 2022–January 2024), categorizing 4528 frames into fissures (516), external lacerations (2174), and internal lacerations (1838), following validation by three experts. Data was split 80% for training and 20% for testing. Performance metrics included sensitivity, specificity, and accuracy.

For external lacerations, the CNN achieved 82.5% sensitivity, 93.5% specificity, and 88.2% accuracy. For internal lacerations, achieved 91.7% sensitivity, 85.9% specificity, and 88.2% accuracy. For anal fissures, achieved 100% sensitivity, specificity, and accuracy.

This first EUAS AI-assisted model for differentiating benign anal injuries demonstrates excellent diagnostic performance. It highlights AI’s potential to improve accuracy, reduce reliance on expertise, and support broader clinical adoption. While currently limited by small dataset and single-center scope, this work represents a significant step towards integrating AI in proctology.

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