通过使用人工智能的心声心脏成像来诊断心脏粉症
Adam Ioannou1, Michel G Khouri2, Takeshi Kitai3
1National Amyloidosis Centre, University College London, Royal Free Campus, London, United Kingdom (A.I., L.V., Y.R., A.P., A.M.-N., M.U.R., H.L., P.N.H., A.W., J.D.G., M. Fontana).
Circulation. Cardiovascular imaging
|February 12, 2026
概括
人工智能 (AI) 改善了心脏粉症 (CA) 在心声回声学上的诊断. 一个AI深度学习模型在外部验证中实现了88.4%的准确性,超过了用于检测CA的传统评分系统.
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:
- 使用回声心脏成像来诊断心脏粉症 (CA) 是具有挑战性的,因为其图像特征与其他缩性疾病重叠.
- 现有的多参数心声回声计分系统需要改进,以准确检测CA.
研究的目的:
- 在现有的心声回声计分系统中评估人工智能 (AI) 衍生的测量,用于CA检测.
- 开发和验证基于人工智能的深度学习模型,用于基于视频的心声回应仪的CA诊断.
主要方法:
- 利用来自多个国际中心的5776名患者 (2756名患有CA,3020名对照) 的大量数据集进行培训和验证.
- 开发了一个基于视频的心声回声图分析的深度学习模型,并将其性能与人工智能增强的多参数评分进行比较.
主要成果:
- 人工智能增强的多参数得分达到79.5% (美国) 和79.7% (日本) 的准确度.
- 人工智能深度学习模型表现出高的内部准确性 (96.2%) 和外部验证准确性 (87.5%在美国,88.4%在日本).
- 深度学习模型与AI增强得分 (AUC,0.88) 相比,显示出更高的诊断性能 (AUC,0.93),并准确地区分了CA与其他过度缩的复印件.
结论:
- 人工智能衍生的测量和深度学习模型都能准确地在全球多样化的人口中识别CA.
- 完全自动化的深度学习模型为通过心声回声学检测心脏粉样性病提供了卓越的诊断准确性.
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