Baloescu Cristiana, Bailitz John, Cheema Baljash, Agarwala Ravi, Jankowski Madeline, Eke Onyinyechi, Liu Rachel, Nomura Jason, Stolz Lori, Gargani Luna, Alkan Eren, Wellman Tyler, Parajuli Nripesh, Marra Andrew, Thomas Yngvil, Patel Daven, Schraft Evelyn, O'Brien James, Moore Christopher L, Gottlieb Michael
Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut.
Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.
JAMA Cardiol. 2025 Mar 1;10(3):245-253. doi: 10.1001/jamacardio.2024.4991.
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.
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.
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.
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.
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%).
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.
ClinicalTrials.gov Identifier: NCT05992324.
肺部超声（LUS）有助于诊断呼吸困难患者，包括心源性肺水肿患者，但图像采集需要技术熟练程度。先前的研究已证明人工智能（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，这表明其在LUS中更广泛应用的潜力。
评估AI指导训练有素的医疗保健专业人员（THCPs）获取诊断质量的LUS图像的能力。
设计、设置和参与者：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岁及以上呼吸急促的参与者接受了2次超声检查：1次由使用肺部引导AI的THCP操作员进行，另1次由没有AI的训练有素的LUS专家进行。THCPs（包括医疗助理、呼吸治疗师和护士）在参与前接受了LUS采集的标准化AI培训。
肺部引导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方案，AI软件自动捕获诊断质量的图像。
主要终点是根据5名盲法LUS专家读者小组确定的THCP采集的诊断质量检查的比例，这些专家提供远程审查和地面真实性验证。
意向性分析纳入了176名参与者（81名女性参与者[46.0%]；平均[标准差]年龄，63[14]岁；平均[标准差]体重指数，31[8]）。总体而言，THCP采集的研究中有98.3%（95%CI，95.1%-99.4%）具有诊断质量，与LUS专家采集的研究相比，质量无统计学显著差异（差异为1.7%；95%CI，-1.6%至5.0%）。
在这项多中心验证研究中，与没有AI的LUS专家相比，有AI协助的THCP获得了符合诊断标准的LUS图像。这项技术可以将LUS的获取范围扩展到缺乏专业人员的服务不足地区。
ClinicalTrials.gov标识符：NCT05992324。
