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Volume 15
Issue 17
10.3390/diagnostics15172145
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Article

AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels

by
Seyed Ehsan Seyed Bolouri
1,
Masood Dehghan
1,
Mahdiar Nekoui
1,
Brian Buchanan
2,
Jacob L. Jaremko
3,
Dornoosh Zonoobi
1,*,
Arun Nagdev
1,4 and
Jeevesh Kapur
5
1
Exo Imaging, Santa Clara, CA 95054, USA
2
Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2B7, Canada
3
Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada
4
Alameda Health System, Highland Hospital, University of California San Francisco, San Francisco, CA 94143, USA
5
Department of Diagnostic Imaging, National University of Singapore, Singapore 119074, Singapore
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(17), 2145; https://doi.org/10.3390/diagnostics15172145
Submission received: 7 July 2025 / Revised: 4 August 2025 / Accepted: 22 August 2025 / Published: 25 August 2025
(This article belongs to the Special Issue Artificial Intelligence for Precision Analysis and Decision Making in Medical Imaging)
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Abstract

Background/Objective: Lung ultrasound (LUS) is a valuable tool for detecting pulmonary conditions, but its accuracy depends on user expertise. This study evaluated whether an artificial intelligence (AI) tool could improve clinician performance in detecting pleural effusion and consolidation/atelectasis on LUS scans. Methods: In this multi-reader, multi-case study, 14 clinicians of varying experience reviewed 374 retrospectively selected LUS scans (cine clips from the PLAPS point, obtained using three different probes) from 359 patients across six centers in the U.S. and Canada. In phase one, readers scored the likelihood (0–100) of pleural effusion and consolidation/atelectasis without AI. After a 4-week washout, they re-evaluated all scans with AI-generated bounding boxes. Performance metrics included area under the curve (AUC), sensitivity, specificity, and Fleiss’ Kappa. Subgroup analyses examined effects by reader experience. Results: For pleural effusion, AUC improved from 0.917 to 0.960, sensitivity from 77.3% to 89.1%, and specificity from 91.7% to 92.9%. Fleiss’ Kappa increased from 0.612 to 0.774. For consolidation/atelectasis, AUC rose from 0.870 to 0.941, sensitivity from 70.7% to 89.2%, and specificity from 85.8% to 89.5%. Kappa improved from 0.427 to 0.756. Conclusions: AI assistance enhanced clinician detection of pleural effusion and consolidation/atelectasis in LUS scans, particularly benefiting less experienced users.
Keywords: lung ultrasound; artificial intelligence; pleural effusion; consolidation; diagnostic accuracy lung ultrasound; artificial intelligence; pleural effusion; consolidation; diagnostic accuracy

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MDPI and ACS Style

Seyed Bolouri, S.E.; Dehghan, M.; Nekoui, M.; Buchanan, B.; Jaremko, J.L.; Zonoobi, D.; Nagdev, A.; Kapur, J. AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels. Diagnostics 2025, 15, 2145. https://doi.org/10.3390/diagnostics15172145

AMA Style

Seyed Bolouri SE, Dehghan M, Nekoui M, Buchanan B, Jaremko JL, Zonoobi D, Nagdev A, Kapur J. AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels. Diagnostics. 2025; 15(17):2145. https://doi.org/10.3390/diagnostics15172145

Chicago/Turabian Style

Seyed Bolouri, Seyed Ehsan, Masood Dehghan, Mahdiar Nekoui, Brian Buchanan, Jacob L. Jaremko, Dornoosh Zonoobi, Arun Nagdev, and Jeevesh Kapur. 2025. "AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels" Diagnostics 15, no. 17: 2145. https://doi.org/10.3390/diagnostics15172145

APA Style

Seyed Bolouri, S. E., Dehghan, M., Nekoui, M., Buchanan, B., Jaremko, J. L., Zonoobi, D., Nagdev, A., & Kapur, J. (2025). AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels. Diagnostics, 15(17), 2145. https://doi.org/10.3390/diagnostics15172145

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