人工智能模型整合了急性冠状动脉综合征的病变诊断和风险评估
Peng Peng Xu1, Bin Hu1, Fan Zhou1
1Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210002, China.
EClinicalMedicine
|August 20, 2025
概括
一个随机森林 (RF) 模型准确诊断了急性冠状动脉综合征 (ACS) 病变,并预测了未来最多两年的ACS风险. 这种机器学习方法改善了心血管风险分层.
科学领域:
- 心血管医学
- 医疗保健中的机器学习
- 诊断成像分析
背景情况:
- 准确的诊断和急性冠状动脉综合征 (ACS) 风险预测对于心血管健康至关重要.
- 目前的风险分层方法在ACS及时诊断和未来风险评估方面存在局限性.
- 对于诊断和预测的综合机器学习 (ML) 方法仍然是一个未满足的需求.
研究的目的:
- 开发和验证使用冠状动脉计算机断层扫描 (CCTA) 诊断ACS罪祸首的ML模型.
- 评估ML模型在最佳时间框架内预测未来ACS风险的能力.
- 将ML模型的性能与传统的狭窄度评估进行比较.
主要方法:
- 一项多中心队列研究涉及回顾性 (衍生性) 和前性 (验证性) 患者数据.
- 评估了五种ML算法来诊断ACS罪祸首的病变;选择了最好的模型.
- 在独立的队列中验证了所选模型的诊断和预测性能.
主要成果:
- 随机森林 (RF) 模型实现了0.85的AUC,用于诊断衍生队列中的ACS罪祸首病变.
- 射频模型有效地对未来的ACS风险进行了分层 (日志等级P<0.001).
- 射频模型在两年ACS风险预测 (AUC和NRI值) 中表现优于狭窄的严重程度.
结论:
- 基于射频的ACS病变模型显示了高的诊断准确性.
- 这种ML模型对短期 (2年) ACS风险分层有效.
- 这些发现支持使用ML来改善心血管风险评估.
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