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Multicenter Study
. 2025 Mar 1;10(3):245-253.
doi: 10.1001/jamacardio.2024.4991.

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts

Cristiana Baloescu  1 , John Bailitz  2 , Baljash Cheema  3 , Ravi Agarwala  4   5 , Madeline Jankowski  3 , Onyinyechi Eke  6 , Rachel Liu  1 , Jason Nomura  7   8 , Lori Stolz  9 , Luna Gargani  10 , Eren Alkan  11 , Tyler Wellman  11 , Nripesh Parajuli  11 , Andrew Marra  11 , Yngvil Thomas  11 , Daven Patel  12 , Evelyn Schraft  12 , James O'Brien  12 , Christopher L Moore  1 , Michael Gottlieb  12
Affiliations
Multicenter Study

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts

Cristiana Baloescu et al. JAMA Cardiol. .

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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Conflict of interest statement

Conflict of Interest Disclosures: Dr Baloescu reported grants from Caption Health during the conduct of the study and grants from Philips outside the submitted work. Dr Bailitz reported grants from Caption Health for prototype development and consulting fees for serving as an expert reader during the conduct of the study. Dr Cheema reported personal fees from Bristol Myers Squibb, Novo Nordisk, and Viz.ai outside the submitted work and serving as an advisor for and holding equity interest in HealthSpan and Zoe Biosciences. Dr Agarwala reported personal fees from Caption Health during the conduct of the study. Drs Jankowski and Liu reported personal fees from Caption Health outside the submitted work. Dr Eke reported consulting fees from Caption Health during the conduct of the study. Dr Nomura reported personal fees from Caption AI during the conduct of the study and from Philips outside the submitted work. Dr Stolz reported personal fees from Caption Health during the conduct of the study and personal fees from the Butterfly Network, Philips Healthcare, and Think Sono outside the submitted work. Dr Gargani reported personal fees from Caption Health during the conduct of the study and from Sanofi outside the submitted work. Drs Alkan and Wellman reported grants from the Bill and Melinda Gates Foundation during the conduct of the study. Dr Wellman and Mr Thomas reported being employed by GE HealthCare during the conduct of the study. Dr Patel reported grants from the Bill and Melinda Gates Foundation via funding for Caption Health to Rush University Medical Center during the conduct of the study. Dr Moore reported grants from GE HealthCare during the conduct of the study and from Philips outside the submitted work. Dr Gottlieb reported grants from the Bill and Melinda Gates Foundation during the conduct of the study. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Overview of Model Development
Overview of model development (left), key features (middle), and user interface with all features (right) for artificial intelligence (AI) Lung Guidance software.
Figure 2.
Figure 2.. Enrollment Diagram
THCP indicates trained health care professionals.
See this image and copyright information in PMC

References

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