Wang Wenwen, Cai Yuyang, Guo Zhe, Zhao Aihua, Ma Wenqing, Wang Wuliang, Wang Shixuan, Zhu Xin, Du Xin, Shen Wenfeng
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, China.
iScience. 2025 Jul 3;28(8):113045. doi: 10.1016/j.isci.2025.113045. eCollection 2025 Aug 15.
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.
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，但传统宫腔镜检查面临准确性挑战。本研究介绍了ECCADx，这是一种基于深度学习的计算机辅助诊断系统，利用对比学习进行宫腔镜检查以识别AEH和EC。这是将对比学习用于这种特定鉴别诊断的系统。ECCADx在对各种外部医学图像进行预训练期间利用对比学习，提取强大的特征。它在来自1204例患者的49646张图像上进行训练，并在两个独立测试数据集（来自190例患者的6228张图像）上进行了严格的多中心验证。ECCADx始终实现了高诊断准确性，常常超过经验丰富的内镜医师。值得注意的是，它在内部数据集上达到了95.2%的灵敏度和91.3%的特异性，在外部数据集上达到了92.1% 的灵敏度和100%的特异性。ECCADx被证明是一种可靠的工具，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预后。
