人工智能辅助的诊断和预后在低射出分数使用心电图在住院病房:一个务实的随机对照试验
Dung-Jang Tsai1,2, Chin Lin1,2, Wei-Ting Liu3
1Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, Republic of China.
BMC medicine
|June 8, 2025
概括
一个人工智能辅助的心电图工具显著改善了住院患者低射出分数 (EF) 的早期诊断. 这种人工智能 (AI) 方法提高了诊断效率,特别是在高风险人群中,而不增加整体医疗保健使用.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 早期诊断低射出分数 (EF) 是至关重要的,但在非心脏病专家的住院患者环境中具有挑战性.
- 人工智能 (AI) 为提高诊断准确性和效率提供了潜在的解决方案.
研究的目的:
- 评估人工智能辅助心电图 (ECG) 工具,用于在住院患者中早期诊断低EF.
- 评估该工具对诊断效率和医疗保健利用的影响.
主要方法:
- 一项实用随机对照试验,涉及13,631名住院患者.
- 干预组接受了人工智能生成的心电图结果;对照组接受了标准护理.
- 主要结局:新诊断的30天内低EF;次要结局:回声心电图使用,预测价值,心脏病咨询.
主要成果:
- AI-ECG工具显著增加了新的低EF诊断 (1.5%与1.1%,HR 1.50,P=0.023).
- 在干预组中,低EF检测的正预测值较高 (34.2%对20.2%,P<0.001).
- 高风险患者心脏病咨询次数增加 (29.3%对23.5%,P=0.027).
结论:
- AI-ECG算法有效地提高了住院患者早期低EF诊断.
- 该工具提高了诊断效率,但没有增加整体医疗保健利用率.
- 人工智能识别了高风险患者,导致了更有针对性的专家咨询和测试.
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