[2603.02704] Intelligent Pathological Diagnosis of Gestational Trophoblastic Diseases via Visual-Language Deep Learning Model

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.02704 (cs) [Submitted on 3 Mar 2026] Title:Intelligent Pathological Diagnosis of Gestational Trophoblastic Diseases via Visual-Language Deep Learning Model Authors:Yuhang Liu, Yueyang Cang, Wenge Que, Xinru Bai, Xingtong Wang, Kuisheng Chen, Jingya Li, Xiaoteng Zhang, Xinmin Li, Lixia Zhang, Pingge Hu, Qiaoting Xie, Peiyu Xu, Xianxu Zeng, Li Shi View a PDF of the paper titled Intelligent Pathological Diagnosis of Gestational Trophoblastic Diseases via Visual-Language Deep Learning Model, by Yuhang Liu and 14 other authors View PDF Abstract:The pathological diagnosis of gestational trophoblastic disease(GTD) takes a long time, relies heavily on the experience of pathologists, and the consistency of initial diagnosis is low, which seriously threatens maternal health and reproductive outcomes. We developed an expert model for GTD pathological diagnosis, named GTDoctor. GTDoctor can perform pixel-based lesion segmentation on pathological slides, and output diagnostic conclusions and personalized pathological analysis results. We developed a software system, GTDiagnosis, based on this technology and conducted clinical trials. The retrospective results demonstrated that GTDiagnosis achieved a mean precision of over 0.91 for lesion detection in pathological slides (n=679 slides). In prospective studies, pathologists using GTDiagnosis attained a Positive Predictive Value of 95.59% (n=68 patients). The tool reduc...