Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

IF 14.8 1区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS JAMA cardiology Pub Date : 2025-01-15 DOI:10.1001/jamacardio.2024.4991

Cristiana Baloescu, John Bailitz, Baljash Cheema, Ravi Agarwala, Madeline Jankowski, Onyinyechi Eke, Rachel Liu, Jason Nomura, Lori Stolz, Luna Gargani, Eren Alkan, Tyler Wellman, Nripesh Parajuli, Andrew Marra, Yngvil Thomas, Daven Patel, Evelyn Schraft, James O'Brien, Christopher L Moore, Michael Gottlieb

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Abstract

Importance: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of artificial intelligence (AI) in guiding novice users to acquire high-quality cardiac ultrasound images, suggesting its potential for broader use in LUS.

Objective: To evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs).

Design, setting, and participants: In this multicenter diagnostic validation study conducted between July 2023 and December 2023, participants aged 21 years or older with shortness of breath recruited from 4 clinical sites underwent 2 ultrasound examinations: 1 examination by a THCP operator using Lung Guidance AI and the other by a trained LUS expert without AI. The THCPs (including medical assistants, respiratory therapists, and nurses) underwent standardized AI training for LUS acquisition before participation.

Interventions: Lung Guidance AI software uses deep learning algorithms guiding LUS image acquisition and B-line annotation. Using an 8-zone LUS protocol, the AI software automatically captures images of diagnostic quality.

Main outcomes and measures: The primary end point was the proportion of THCP-acquired examinations of diagnostic quality according to a panel of 5 masked expert LUS readers, who provided remote review and ground truth validation.

Results: The intention-to-treat analysis included 176 participants (81 female participants [46.0%]; mean [SD] age, 63 [14] years; mean [SD] body mass index, 31 [8]). Overall, 98.3% (95% CI, 95.1%-99.4%) of THCP-acquired studies were of diagnostic quality, with no statistically significant difference in quality compared to LUS expert-acquired studies (difference, 1.7%; 95% CI, -1.6% to 5.0%).

Conclusions and relevance: In this multicenter validation study, THCPs with AI assistance achieved LUS images meeting diagnostic standards compared with LUS experts without AI. This technology could extend access to LUS to underserved areas lacking expert personnel.

Trial registration: ClinicalTrials.gov Identifier: NCT05992324.

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非专业人士的人工智能引导肺部超声。

重要性：肺超声（LUS）有助于诊断呼吸困难患者，包括心源性肺水肿患者，但需要熟练的图像采集技术。先前的研究已经证明了人工智能（AI）在指导新手用户获取高质量心脏超声图像方面的有效性，这表明它在LUS中有更广泛的应用潜力。目的：评价人工智能对训练有素的卫生保健专业人员（THCPs）获取诊断质量LUS图像的指导能力。设计、环境和参与者：在这项于2023年7月至2023年12月进行的多中心诊断验证研究中，从4个临床地点招募的21岁或以上呼吸短促的参与者进行了两次超声检查：一次由THCP操作员使用肺引导AI进行检查，另一次由训练有素的LUS专家进行检查。thcp（包括医疗助理、呼吸治疗师和护士）在参与前接受了标准化的LUS获取人工智能培训。干预措施：Lung Guidance AI软件使用深度学习算法指导LUS图像采集和b线标注。使用8区LUS协议，人工智能软件自动捕获诊断质量的图像。主要结局和措施：主要终点是由5名蒙面专家LUS读者组成的小组提供远程审查和地面真相验证，thcp获得诊断质量检查的比例。结果：意向治疗分析纳入176名受试者(女性81名[46.0%]；平均[SD]年龄63岁；平均[SD]体重指数31[8])。总体而言，98.3% （95% CI, 95.1%-99.4%）的thcp获得性研究具有诊断质量，与LUS专家获得性研究相比，质量无统计学差异(差异，1.7%；95% CI, -1.6% ~ 5.0%)。结论和相关性：在这项多中心验证研究中，与没有人工智能的LUS专家相比，人工智能辅助下的thcp获得了符合诊断标准的LUS图像。这项技术可以将LUS扩展到缺乏专业人员的服务不足的地区。试验注册：ClinicalTrials.gov标识符：NCT05992324。

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来源期刊

JAMA cardiology Medicine-Cardiology and Cardiovascular Medicine

CiteScore

45.80

自引率

1.70%

发文量

264

期刊介绍： JAMA Cardiology, an international peer-reviewed journal, serves as the premier publication for clinical investigators, clinicians, and trainees in cardiovascular medicine worldwide. As a member of the JAMA Network, it aligns with a consortium of peer-reviewed general medical and specialty publications. Published online weekly, every Wednesday, and in 12 print/online issues annually, JAMA Cardiology attracts over 4.3 million annual article views and downloads. Research articles become freely accessible online 12 months post-publication without any author fees. Moreover, the online version is readily accessible to institutions in developing countries through the World Health Organization's HINARI program. Positioned at the intersection of clinical investigation, actionable clinical science, and clinical practice, JAMA Cardiology prioritizes traditional and evolving cardiovascular medicine, alongside evidence-based health policy. It places particular emphasis on health equity, especially when grounded in original science, as a top editorial priority.