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

IF 2.9 3区医学 Q2 GASTROENTEROLOGY & HEPATOLOGY

Techniques in Coloproctology Pub Date : 2025-06-10 DOI:10.1007/s10151-025-03160-0

M Mascarenhas, M J Almeida, M Martins, F Mendes, J Mota, P Cardoso, B Mendes, J Ferreira, G Macedo, C Poças

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引用次数: 0

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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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人工智能和肛门内超声：开创了肛门和括约肌良性病变的自动鉴别。

背景：肛门损伤，如撕裂和裂缝，是具有挑战性的诊断，因为他们的解剖复杂性。肛内超声（EAUS）已被证明是肛门结构详细可视化的可靠工具，但依赖于专家的解释。人工智能（AI）可能会为更准确、更一致的诊断提供解决方案。本研究旨在开发和测试一种基于卷积神经网络（CNN）的算法，用于在EUAS上自动分类裂缝和肛门撕裂伤（内部和外部）。方法：单中心回顾性研究分析了238例EUAS桡骨探头检查（2022年4月- 2024年1月），经三名专家验证，将4528例框架分为裂伤（516例）、外裂伤（2174例）和内裂伤（1838例）。数据分成80%用于训练，20%用于测试。性能指标包括敏感性、特异性和准确性。结果：对于外部撕裂伤，CNN的敏感性为82.5%，特异性为93.5%，准确性为88.2%。对于内部撕裂伤，灵敏度为91.7%，特异性为85.9%，准确性为88.2%。对于肛裂，达到100%的敏感性、特异性和准确性。结论：该EUAS人工智能辅助模型具有良好的诊断性能。它强调了人工智能在提高准确性、减少对专业知识的依赖以及支持更广泛的临床应用方面的潜力。虽然目前受到小数据集和单中心范围的限制，但这项工作代表了将人工智能集成到肠系学中的重要一步。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Techniques in Coloproctology GASTROENTEROLOGY & HEPATOLOGY-SURGERY

CiteScore

5.30

自引率

9.10%

发文量

176

审稿时长

1 months

期刊介绍： Techniques in Coloproctology is an international journal fully devoted to diagnostic and operative procedures carried out in the management of colorectal diseases. Imaging, clinical physiology, laparoscopy, open abdominal surgery and proctoperineology are the main topics covered by the journal. Reviews, original articles, technical notes and short communications with many detailed illustrations render this publication indispensable for coloproctologists and related specialists. Both surgeons and gastroenterologists are represented on the distinguished Editorial Board, together with pathologists, radiologists and basic scientists from all over the world. The journal is strongly recommended to those who wish to be updated on recent developments in the field, and improve the standards of their work. Manuscripts submitted for publication must contain a statement to the effect that all human studies have been reviewed by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards laid down in an appropriate version of the 1965 Declaration of Helsinki. It should also be stated clearly in the text that all persons gave their informed consent prior to their inclusion in the study. Details that might disclose the identity of the subjects under study should be omitted. Reports of animal experiments must state that the Principles of Laboratory Animal Care (NIH publication no. 86-23 revised 1985) were followed as were applicable national laws (e.g. the current version of the German Law on the Protection of Animals). The Editor-in-Chief reserves the right to reject manuscripts that do not comply with the above-mentioned requirements. Authors will be held responsible for false statements or for failure to fulfill such requirements.