动脉多普勒波形的人工智能预测了对外围动脉疾病评估的患者的主要不良结果
Robert D McBane1,2, Dennis H Murphree3, David Liedl1
1Gonda Vascular Center Mayo Clinic Rochester MN.
Journal of the American Heart Association
|January 19, 2024
概括
分析多普勒波形的深度神经网络可以预测外围动脉疾病患者的主要不良事件. 这种人工智能工具有助于识别高风险个体,以改善心血管和四肢事件的结果.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 周围动脉疾病 (PAD) 显著增加了主要不良心脏事件 (MACE),主要不良肢体事件 (MALE) 和全因死亡率的风险.
- 识别高风险PAD患者对于实施预防策略和改善患者治疗结果至关重要.
- 目前的风险分层方法可能无法完全捕捉PAD相关不良事件的复杂病理生理学.
研究的目的:
- 评估深度神经网络 (DNN) 在分析静止多普勒波形以预测PAD患者不良结果时的有效性.
- 确定人工智能驱动的后动脉多普勒波形分析是否可以准确地识别5年内患有MACE,MALE和死亡高风险的患者.
主要方法:
- 包括一组接受脚-手臂指数测试的患者,使用DNNs分析多普勒波形.
- 在5年的随访中,DNN被训练在静止后关动脉的多普勒波形上,以预测MACE,MALE和全因死亡.
- 患者根据DNN预测得分分层分为四分之一,以评估风险水平.
主要成果:
- 该研究包括10,437名患者,随访5年显示447例死亡,585例MACE和161例MALE.
- 在调整了临床因素后,对后阴动脉波形的DNN分析独立预测了5年后的死亡 (HR 2.44),MACE (HR 1.97) 和MALE (HR 11.03),经过对临床因素的调整.
- 人工智能支持的波形分析表明,对PAD患者的不良结果具有显著的预测能力.
结论:
- 对多普勒动脉波形的人工智能分析是一个有前途的工具,用于识别患有外围动脉疾病的患者,这些患者有很高的重大不良结果风险.
- 这种人工智能驱动的方法可以促进早期干预和在PAD管理中遵守风险因素修改策略.
- 对血管波形的AI驱动分析提供了一种新的,非侵入性的方法,用于对外围动脉疾病的风险分层.
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