Intelligent Pathological Diagnosis of Gestational Trophoblastic Diseases via Visual-Language Deep Learning Model
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866915830908846080 |
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| author | Liu, Yuhang Cang, Yueyang Que, Wenge Bai, Xinru Wang, Xingtong Chen, Kuisheng Li, Jingya Zhang, Xiaoteng Li, Xinmin Zhang, Lixia Hu, Pingge Xie, Qiaoting Xu, Peiyu Zeng, Xianxu Shi, Li |
| author_facet | Liu, Yuhang Cang, Yueyang Que, Wenge Bai, Xinru Wang, Xingtong Chen, Kuisheng Li, Jingya Zhang, Xiaoteng Li, Xinmin Zhang, Lixia Hu, Pingge Xie, Qiaoting Xu, Peiyu Zeng, Xianxu Shi, Li |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02704 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Intelligent Pathological Diagnosis of Gestational Trophoblastic Diseases via Visual-Language Deep Learning Model Liu, Yuhang Cang, Yueyang Que, Wenge Bai, Xinru Wang, Xingtong Chen, Kuisheng Li, Jingya Zhang, Xiaoteng Li, Xinmin Zhang, Lixia Hu, Pingge Xie, Qiaoting Xu, Peiyu Zeng, Xianxu Shi, Li Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Intelligent Pathological Diagnosis of Gestational Trophoblastic Diseases via Visual-Language Deep Learning Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.02704 |