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

TL;DR

This paper introduces GTDoctor, a deep learning model for rapid, accurate, and interpretable pathological diagnosis of gestational trophoblastic diseases, significantly improving efficiency and consistency in clinical settings.

Contribution

The study presents a novel pixel-based lesion segmentation model and a software system that enhances diagnostic accuracy and speed for GTD, validated through clinical trials.

Findings

Achieved over 0.91 mean precision in lesion detection

Attained 95.59% positive predictive value in prospective studies

Reduced diagnostic time from 56 to 16 seconds per case

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.