超声心动图发现与死亡率的关联:人体评估与自动化深度学习分析
Roei Merin1, Moran Gvili Perelman2, Hila Merin3
1Internal Medicine Division, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
European heart journal. Digital health
|February 5, 2026
概括
人工智能 (AI) 在心声回声学中显示出与人类分析的强烈相关性. 使用自动化菌株分析的人工智能比单靠人类解释更好地预测患者的死亡率.
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:
- 心声谱对于心脏评估至关重要.
- 人工智能有助于提高心声回声分析的效率和减少心声回声分析的变化.
- 有限的现实数据存在,将AI分析与人类专家和临床结果进行比较.
研究的目的:
- 评估基于人工智能和人类专家回声心脏分析之间的相关性.
- 为了比较人工智能和人类分析与住院患者的一年死亡率的关联.
主要方法:
- 对889名接受心声扫描的住院患者进行了回顾性分析.
- 由人类专家和商业AI软件 (Us2.ai) 分析的心声数据.
- 对心声回声学变量的相关性分析和1年死亡预测模型的比较.
主要成果:
- 在大多数人类和人工智能衍生的心声回声测量结果之间观察到强烈的相关性.
- 人工智能衍生的左心室射出分数 (LVEF) 显著高于人类估计 (P < 0.001).
- 人工智能和人类模型显示了可比的1年死亡率预测 (AUC0.67与0.66).
- 结合自动化菌株分析的人工智能显著改善了死亡率预测 (AUC 0.73 与 0.66 相比,P = 0.048).
结论:
- 基于人工智能的心声回声分析显示了与人类解释的强烈一致.
- 人工智能分析,特别是自动化菌株分析,与标准人类分析相比,与死亡率预测有更好的关联.
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