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多模式深度学习集成用于预测活体捐赠脏移植中的功能结果:一个回顾性队列研究
Jin-Myung Kim1, HyoJe Jung2, Hye Eun Kwon1
1Division of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
International journal of surgery (London, England)
|September 17, 2025
概括
结合CT扫描,放射学报告和临床数据的新型深度学习模型显著改善了移植后功能预测. 这种方法提高了活体捐赠接受者的精密医学.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 精确预测移植后的功能对于移植 (KT) 结果至关重要.
- 仅使用结构化临床数据的传统模型在捕捉复杂的生物因素方面存在局限性.
- 需要先进的方法来整合不同的数据类型,以提高预测准确度.
研究的目的:
- 开发和验证一种多式深度学习模型,用于预测活体捐赠移植 (LDKT) 中1年估计的膜过率 (eGFR).
- 整合计算机断层扫描 (CT) 成像,放射学报告文本和结构化临床变量.
- 评估与仅基于临床数据的模型对比多式联络模型的性能.
主要方法:
- 对1937名LDKT接受者的回顾性分析.
- 整合CT成像 (基于CLIP的视觉提取器) 和放射学报告 (BioBERT嵌入) 与结构化临床数据.
- 用于预测的集合分类器 (XGBoost);使用交叉验证和SHAP分析评估性能.
主要成果:
- 多模式模型实现了0.675的宏F1得分,显著超过仅临床模型 (宏F1 = 0.292).
- CT成像提供了比文本数据更大的贡献.
- 确定的主要预测因素包括捐赠者的年龄,BMI和捐赠者的性别.
结论:
- 综合临床,成像和文本数据的多式深度学习增强了移植后功能预测.
- 该框架为LDKT的个性化风险分层提供了一个强大的和可解释的方法.
- 支持精准医学在器官移植方面的进步.
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