人工智能辅助诊断球炎使用病理图像分析方法:一个多中心模型开发和验证研究
Sheng Nie1, Nan Jia1, Haobo Chen2,3
1Division of Nephrology, Nanfang Hospital, Southern Medical University, National Clinical Research Center for Kidney Disease, State Key Laboratory of Multi-organ Injury Prevention and Treatment, Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou, China.
EClinicalMedicine
|October 23, 2025
概括
开发了一种人工智能 (AI) 模型,从脏活检图像中诊断出四种类型的球炎 (GN),显示出高准确度. 这个人工智能工具旨在通过自动化诊断来帮助病理学家,潜在地提高效率和减少工作量.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 病理学 病理学 病理学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 淋巴细胞炎 (GN) 诊断依赖于手动脏活检分析,这是主观和劳动密集的,导致观察者间的变化.
- 开发一种自动化诊断工具可以提高GN诊断的客观性和效率.
研究的目的:
- 开发和验证一个人工智能 (AI) 辅助的模型,用于使用数字组织学图像诊断四种常见的GN类型.
- 该模型旨在细分淋巴细胞,提取特征,并对包括IgA脏病 (IgAN),膜性脏病 (MN),最小变化疾病 (MCD) 和焦点细分淋巴细胞硬化症 (FSGS) 在内的疾病进行分类.
主要方法:
- 一项多中心研究使用了来自三个机构的6682名患者的106,988张质细胞图像进行模型开发和验证.
- 人工智能模型包括三个模块:球本地化 (GloSNet),用于病变分类的特征提取/融合,以及患者级诊断.
- 使用F1分数,精度和训练回忆,内部验证和两个外部验证队列来评估性能.
主要成果:
- 人工智能模型在外部验证队列中表现出强的表现,F1得分分为83.86%和85.45%,精度分别为81.37%和83.12%,回忆率分别为87.84%和88.94%.
- 该模型成功地细分了淋巴细胞,识别了病变,并高精度地分类了四种类型的GN.
结论:
- 一个人工智能辅助的模型可以从脏活检图像中准确诊断四种常见的GN亚型,从而有可能减少病理学家的工作量并提高诊断效率.
- 需要进一步的研究来解决模型的解释性,结合多种染色方法,并扩大诊断的GN亚型的范围.
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