用视觉语言模型辅助胎盘疾病病理诊断，提升准确率与效率。
Intelligent Pathological Diagnosis of Gestational Trophoblastic Diseases via Visual-Language Deep Learning Model
- 结合视觉与语言的深度学习模型，自动分割病灶并生成诊断报告。
- 病灶检测平均精确率达91%以上，阳性预测值达95.59%。
- 适合病理医生使用，显著缩短诊断时间至16秒/例。
妊娠滋养细胞疾病（GTD）的病理诊断耗时长、依赖经验且初诊一致性低，严重威胁母婴健康与生育结局。我们开发了名为GTDoctor的专家级诊断模型，可对病理切片进行像素级病灶分割，并输出诊断结论与个性化分析结果。基于该技术构建的GTDiagnosis系统已开展临床试验：回顾性研究显示，其在679张切片上的病灶检测平均精确率超过0.91；前瞻性研究中，使用该工具的病理医生实现95.59%的阳性预测值（n=68患者）。诊断平均耗时从56秒降至16秒/例（n=285患者）。GTDoctor与GTDiagnosis为GTD病理诊断提供新方案，在保证临床可解释性的前提下，显著提升诊断性能与效率。
原文摘要 · Abstract (English)
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 reduced average diagnostic time from 56 to 16 seconds per case (n=285 patients). GTDoctor and GTDiagnosis offer a novel solution for GTD pathological diagnosis, enhancing diagnostic performance and efficiency while maintaining clinical interpretability.
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