人工智能全自动手持式超声心动图分析现实世界中疑似心力衰竭患者。

IF 10.8 1区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

European Journal of Heart Failure Pub Date : 2025-07-24 DOI:10.1002/ejhf.3783

Ross T Campbell,Mark C Petrie,Kieran F Docherty,Katriona J M Brooksbank,Gemma McKinley,Caroline Haig,Alex McConnachie,Carolyn S P Lam,Carly Adamson,Elaine Butler,James P Curtain,Nick Hartshorne-Evans,Fraser J Graham,Helen Hainey,John Jarvie,Matthew M Y Lee,Leeanne Macklin,Kenneth Mangion,Aimee McCoubrey,Kirsty McDowell,Aileen McIntyre,Sabrina Nordin,Joanna Osmanska,Pierpaolo Pellicori,Joanne Simpson,Piotr Sonecki,Karen Taylor,Daniel Taylor-Sweet,Pamela Turnbull,Paul Welsh,John J V McMurray,Clare L Murphy,David J Lowe

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引用次数: 0

摘要

超声心动图是心衰（HF）及时诊断的限速步骤。超声心动图的自动报告有可能简化工作流程。本研究的目的是测试全自动人工智能（AI）分析使用手持超声心动图获得的图像的诊断准确性，以及它与专家分析的基于小车的超声心动图在疑似心衰的现实世界队列中的互换性。方法和结果在这项多中心、前瞻性、观察性研究中，疑似心衰患者进行了两次超声心动图检查：一次是手持式便携式超声心动图，一次是基于小车的超声心动图。两种超声心动图均由全自动人工智能软件和人类超声专家进行分析。主要终点是手持式超声心动图人工智能自动分析检测左室射血分数(LVEF)≤40%的诊断准确性。其他终点包括人工智能自动化和人工分析基于车的LVEF之间的互换性（使用个体等效系数[IEC]进行评估）。共有867名患者参与。人工智能自动分析在61%的手持扫描和77%的基于推车的扫描中产生了LVEF，相比之下，人工分析的手持扫描和基于推车的扫描分别为76%和77%。手持式超声心动图的人工智能自动分析在识别LVEF≤40%时的诊断准确率为0.93(95%置信区间[CI] 0.90, 0.95)（与基于小车的经胸超声心动图扫描的人工分析相比）。手持设备上的人工智能自动LVEF分析与两位专家报告的基于小车的LVEF可互换（IEC -0.40, 95% CI -0.60, -0.16）。结论手持式超声心动图人工智能-自动化分析诊断LVEF准确率≤40%。手持式扫描的LVEF的人工智能自动分析与基于购物车的专家分析是可互换的。

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Artificial intelligence fully automated analysis of handheld echocardiography in real-world patients with suspected heart failure.

AIMS Echocardiography is a rate-limiting step in the timely diagnosis of heart failure (HF). Automated reporting of echocardiograms has the potential to streamline workflow. The aim of this study was to test the diagnostic accuracy of fully automated artificial intelligence (AI) analysis of images acquired using handheld echocardiography and its interchangeability with expert human-analysed cart-based echocardiograms in a real-world cohort with suspected HF. METHODS AND RESULTS In this multicentre, prospective, observational study, patients with suspected HF had two echocardiograms: one handheld portable and one cart-based scan. Both echocardiograms were analysed using fully automated AI software and by human expert sonographers. The primary endpoint was the diagnostic accuracy of AI-automated analysis of handheld echocardiography to detect left ventricular ejection fraction (LVEF) ≤40%. Other endpoints included the interchangeability (assessed using individual equivalence coefficient [IEC]), between AI-automated and human analysis of cart-based LVEF. A total of 867 patients participated. The AI-automated analysis produced an LVEF in 61% of the handheld scans and 77% of the cart-based scans, compared to 76% and 77% of human analyses of the handheld and cart-based scans, respectively. The AI-automated analysis of handheld echocardiography had a diagnostic accuracy of 0.93 (95% confidence interval [CI] 0.90, 0.95) for identifying LVEF ≤40% (compared to the human analysis of cart-based transthoracic echocardiography scans). AI-automated analysis of LVEF on handheld devices was interchangeable with cart-based LVEF reported by two expert humans (IEC -0.40, 95% CI -0.60, -0.16). CONCLUSIONS Artificial intelligence-automated analysis of handheld echocardiography had good diagnostic accuracy for detecting LVEF ≤40%. AI-automated analysis of LVEF of handheld scans was interchangeable with cart-based expert human analysis.

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来源期刊

European Journal of Heart Failure 医学-心血管系统

CiteScore

27.30

自引率

11.50%

发文量

365

审稿时长

1 months

期刊介绍： European Journal of Heart Failure is an international journal dedicated to advancing knowledge in the field of heart failure management. The journal publishes reviews and editorials aimed at improving understanding, prevention, investigation, and treatment of heart failure. It covers various disciplines such as molecular and cellular biology, pathology, physiology, electrophysiology, pharmacology, clinical sciences, social sciences, and population sciences. The journal welcomes submissions of manuscripts on basic, clinical, and population sciences, as well as original contributions on nursing, care of the elderly, primary care, health economics, and other related specialist fields. It is published monthly and has a readership that includes cardiologists, emergency room physicians, intensivists, internists, general physicians, cardiac nurses, diabetologists, epidemiologists, basic scientists focusing on cardiovascular research, and those working in rehabilitation. The journal is abstracted and indexed in various databases such as Academic Search, Embase, MEDLINE/PubMed, and Science Citation Index.