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

2026-03-03

AI Summary

This study addresses critical challenges in the pathological diagnosis of gestational trophoblastic disease (GTD)—including prolonged turnaround time, heavy reliance on expert experience, and low inter-observer agreement at initial diagnosis—that significantly compromise maternal health outcomes. To overcome these limitations, we propose GTDoctor, the first vision–language deep learning model specifically designed for GTD, which integrates pixel-level lesion segmentation with natural language generation to deliver highly accurate and interpretable automated diagnoses. Integrated into the GTDiagnosis clinical decision support system, GTDoctor achieved a mean lesion detection precision of 0.91 in a retrospective study of 679 whole-slide images. In a prospective cohort of 68 cases, it demonstrated a positive predictive value of 95.59%, while reducing average diagnostic time per case from 56 to 16 seconds across 285 cases, substantially enhancing both diagnostic efficiency and consistency.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Segmentation
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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 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.

Problem

• Gestational Trophoblastic Disease
• Pathological Diagnosis
• Diagnostic Consistency
• Maternal Health
• Reproductive Outcomes

Innovation

• visual-language deep learning
• pixel-level lesion segmentation
• pathological diagnosis
• clinical interpretability
• gestational trophoblastic disease