通过人工智能增强检测严重的大动脉狭窄:一项临床队列研究
Geoff Strange1,2, Simon Stewart3,4, Andrew Watts5
1Cardiology, Heart Research Institute Ltd, Newtown, New South Wales, Australia gstrange@neda.net.au.
Open heart
|July 25, 2023
概括
人工智能决策支持算法 (AI-DSA) 通过回声心脏图识别了与高死亡率相关的严重大动脉狭窄 (AS) 表型. 这种人工智能工具有助于更早地检测AS患者的疾病并推治疗.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 严重的大动脉狭窄 (AS) 是一种与高死亡率相关的严重疾病.
- 准确识别严重AS表型对于及时干预至关重要.
- 常规心声回声扫描为AS评估提供了有价值的数据.
研究的目的:
- 开发和验证人工智能决策支持算法 (AI-DSA).
- 利用常规的心声回声测量来识别与生存率差相关的严重AS表型.
- 评估AI-DSA在预测严重AS和患者死亡率方面的表现.
主要方法:
- 一个混合密度网络 (MDN) 模型被训练在442,276个人的心声回声数据上,以创建AI-DSA.
- AI-DSA预测大动脉面积<1厘米2,不包括特定测量.
- 算法性能在184,301个人的独立队列中进行了评估.
主要成果:
- AI-DSA在检测严重的AS方面表现出高准确性,接收器操作特征曲线下的面积为0.986.
- 由AI-DSA分类为"高概率"严重AS的个体的5年死亡率 (67.9%) 与低概率组 (19.2%) 相比显著更高.
- AI-DSA确定了与生存率差相关的严重AS表型,包括一些不符合指南定义的表型.
结论:
- 一个AI-DSA可以有效地识别与死亡率增加相关的AS的心声学特征.
- 这种人工智能工具有可能提高在临床实践中加快识别严重AS病例的速度.
- 通过部署这个AI-DSA,可以提升及时转诊治疗.
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