A deep learning-based computer-aided diagnosis system for detecting atypical endometrial hyperplasia and endometrial cancer through hysteroscopy

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2025-07-03 DOI:10.1016/j.isci.2025.113045

Wenwen Wang , Yuyang Cai , Zhe Guo , Aihua Zhao , Wenqing Ma , Wuliang Wang , Shixuan Wang , Xin Zhu , Xin Du , Wenfeng Shen

{"title":"A deep learning-based computer-aided diagnosis system for detecting atypical endometrial hyperplasia and endometrial cancer through hysteroscopy","authors":"Wenwen Wang , Yuyang Cai , Zhe Guo , Aihua Zhao , Wenqing Ma , Wuliang Wang , Shixuan Wang , Xin Zhu , Xin Du , Wenfeng Shen","doi":"10.1016/j.isci.2025.113045","DOIUrl":null,"url":null,"abstract":"<div><div>Timely diagnosis of endometrial cancer (EC) and atypical endometrial hyperplasia (AEH) is crucial, yet traditional hysteroscopy faces accuracy challenges. This study introduces ECCADx, a deep learning-based computer-aided diagnosis system utilizing contrastive learning for hysteroscopic identification of AEH and EC. This is the system to integrate contrastive learning for this specific differentiation. ECCADx leveraged contrastive learning during pre-training on diverse external medical images, extracting robust features. Trained on 49,646 images from 1,204 patients, it underwent rigorous multicenter validation on two independent test datasets (6,228 images from 190 patients). ECCADx consistently achieved high diagnostic accuracy, often surpassing experienced endoscopists. Notably, it attained 95.2% sensitivity and 91.3% specificity on the internal dataset, and 92.1% sensitivity with 100% specificity on the external dataset. ECCADx proves a reliable tool, comparable or superior to human experts, promising to reduce misdiagnosis and improve patient outcomes.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"28 8","pages":"Article 113045"},"PeriodicalIF":4.6000,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"iScience","FirstCategoryId":"103","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589004225013069","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Timely diagnosis of endometrial cancer (EC) and atypical endometrial hyperplasia (AEH) is crucial, yet traditional hysteroscopy faces accuracy challenges. This study introduces ECCADx, a deep learning-based computer-aided diagnosis system utilizing contrastive learning for hysteroscopic identification of AEH and EC. This is the system to integrate contrastive learning for this specific differentiation. ECCADx leveraged contrastive learning during pre-training on diverse external medical images, extracting robust features. Trained on 49,646 images from 1,204 patients, it underwent rigorous multicenter validation on two independent test datasets (6,228 images from 190 patients). ECCADx consistently achieved high diagnostic accuracy, often surpassing experienced endoscopists. Notably, it attained 95.2% sensitivity and 91.3% specificity on the internal dataset, and 92.1% sensitivity with 100% specificity on the external dataset. ECCADx proves a reliable tool, comparable or superior to human experts, promising to reduce misdiagnosis and improve patient outcomes.

Abstract Image

查看原文本刊更多论文

基于深度学习的宫腔镜非典型子宫内膜增生和子宫内膜癌计算机辅助诊断系统

子宫内膜癌（EC）和非典型子宫内膜增生（AEH）的及时诊断至关重要，但传统宫腔镜的准确性面临挑战。本研究介绍了一种基于深度学习的计算机辅助诊断系统ECCADx，该系统利用对比学习技术用于宫腔镜下AEH和EC的识别。这就是系统整合对比学习对于这个具体的微分。ECCADx利用对比学习对各种外部医学图像进行预训练，提取鲁棒性特征。它在1204名患者的49646张图像上进行了训练，在两个独立的测试数据集（190名患者的6228张图像）上进行了严格的多中心验证。ECCADx始终实现高诊断准确性，通常超过经验丰富的内窥镜医师。值得注意的是，它在内部数据集上达到95.2%的灵敏度和91.3%的特异性，在外部数据集上达到92.1%的灵敏度和100%的特异性。事实证明，ECCADx是一种可靠的工具，可与人类专家相媲美或优于人类专家，有望减少误诊并改善患者的治疗效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

期刊介绍： Science has many big remaining questions. To address them, we will need to work collaboratively and across disciplines. The goal of iScience is to help fuel that type of interdisciplinary thinking. iScience is a new open-access journal from Cell Press that provides a platform for original research in the life, physical, and earth sciences. The primary criterion for publication in iScience is a significant contribution to a relevant field combined with robust results and underlying methodology. The advances appearing in iScience include both fundamental and applied investigations across this interdisciplinary range of topic areas. To support transparency in scientific investigation, we are happy to consider replication studies and papers that describe negative results. We know you want your work to be published quickly and to be widely visible within your community and beyond. With the strong international reputation of Cell Press behind it, publication in iScience will help your work garner the attention and recognition it merits. Like all Cell Press journals, iScience prioritizes rapid publication. Our editorial team pays special attention to high-quality author service and to efficient, clear-cut decisions based on the information available within the manuscript. iScience taps into the expertise across Cell Press journals and selected partners to inform our editorial decisions and help publish your science in a timely and seamless way.