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一个多标准的功能整合框架,用于准确诊断Sjögren病,使用常规实验室测试

Shu Liu¹, Guanhao Wu¹, Mengge Pan¹

¹Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.

NPJ digital medicine

|November 27, 2025

概括

一个新的AI模型,Sjögren多标准特征集成框架 (SMFIF),可以帮助诊断Sjögren.

科学领域:

类风湿病学类风湿病学
自免疫性疾病自免疫性疾病
人工智能在医学中的应用

背景情况:

由于非特异性症状和有限的生物标志物,Sjögren病 (SjD) 诊断具有挑战性.
早期诊断对于管理SjD和预防并发症至关重要.

研究的目的:

评估Sjögren病常规实验室检测的诊断潜力.
开发和验证用于SjD诊断的具有成本效益的人工智能 (AI) 模型.

主要方法:

一项多中心的回顾性队列研究,涉及来自中国三家医院的34,958名患者.
开发Sjögren多标准特征集成框架 (SMFIF) 使用集体学习和SHAP分析,具有16个最佳特征.
对SMFIF模型的内部和外部验证.

主要成果:

SMFIF表现出高的诊断性能,AUC为0.929 (测试),0.934 (内部验证) 和0.964 (外部验证).
人工智能模型的表现优于传统生物标志物,如抗SSA/Ro和抗核抗体 (ANA).
校准曲线和混矩阵证实了模型的可靠性.

结论:

SMFIF提供了一个强大的,低成本的AI驱动的诊断工具,用于Sjögren病.
该模型根据现有实验室数据提供SjD概率.
SMFIF代表了对Sjögren病的临床诊断的实际进展.

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