{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,14]],"date-time":"2026-11-14T10:07:51Z","timestamp":1794650871618,"version":"4.9.1"},"reference-count":44,"publisher":"American Medical Association (AMA)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Cardiol"],"published-print":{"date-parts":[[2025,3,1]]},"abstract":"\n Importance<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs).<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Interventions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n 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\u2013acquired studies (difference, 1.7%; 95% CI, \u22121.6% to 5.0%).<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Trial Registration<\/jats:title>\n \n ClinicalTrials.gov Identifier:\n NCT05992324<\/jats:ext-link>\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamacardio.2024.4991","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T11:01:02Z","timestamp":1736938862000},"page":"245","source":"Crossref","is-referenced-by-count":65,"title":["Artificial Intelligence\u2013Guided Lung Ultrasound by Nonexperts"],"prefix":"10.1001","volume":"10","author":[{"given":"Cristiana","family":"Baloescu","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Bailitz","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, 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