已故捐赠者的脏移植结果预测 使用人工智能帮助脏分配决策
Hatem Ali1, Mahmoud Mohamed2, Miklos Z Molnar3
1From the University Hospitals of Coventry and Warwickshire, United Kingdom.
ASAIO journal (American Society for Artificial Internal Organs : 1992)
|March 29, 2024
概括
人工智能 (AI) 开发了移植的新风险指数,其表现优于目前的模型. 这种人工智能工具D-TOP改善了已故捐赠者的选择,以改善移植受体的移植存活率.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 移植免疫学 移植免疫学
- 医学中的人工智能
背景情况:
- 优化移植接受者与捐赠者的配对,以实现移植的存活是非常具有挑战性的.
- 现有的风险预测模型的准确性和校准性有限.
- 现代决策支持工具与传统模型相比并没有得到充分的比较.
研究的目的:
- 使用人工智能 (AI) 开发一个高度准确的风险分层指数.
- 将人工智能驱动的风险预测与现有模型比较,例如脏捐赠者概况指数 (KDPI).
- 加强已故捐赠者选择和分配计划.
主要方法:
- 利用UNOS数据库 (156,749例已故脏移植,2007-2021年) 进行培训和验证.
- 开发和评估了四种机器学习模型,包括Deep Cox混合物.
- 通过使用歧视 (CTD) 和校准 (IBS) 度量来评估死亡审查的移植存活模型.
- 使用英国移植数据进行决策曲线分析和外部验证.
主要成果:
- 基于AI的Deep Cox混合模型显示出优异的区分性能 (AUC 0.66-0.68) 和CTD (0.66).
- 基于AI的D-TOP显著超过了KDPI模型 (CTD0.59,AUC0.60).
- 人工智能模型的IBS为0.12,实现了充分的校准.
结论:
- 基于人工智能的D-TOP是比KDPI更有效的工具来评估移植对.
- 这种人工智能方法可能会改善已故供体的选择,并优化移植的存活率.
- 预计先进的计算,包括人工智能,将对未来的分配策略产生重大影响.
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