{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T10:48:25Z","timestamp":1782125305476,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T00:00:00Z","timestamp":1756080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Exo"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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\u2013100) 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\u2019 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\u2019 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.<\/jats:p>","DOI":"10.3390\/diagnostics15172145","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T06:25:57Z","timestamp":1756189557000},"page":"2145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8869-7214","authenticated-orcid":false,"given":"Seyed Ehsan","family":"Seyed Bolouri","sequence":"first","affiliation":[{"name":"Exo Imaging, Santa Clara, CA 95054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masood","family":"Dehghan","sequence":"additional","affiliation":[{"name":"Exo Imaging, Santa Clara, CA 95054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdiar","family":"Nekoui","sequence":"additional","affiliation":[{"name":"Exo Imaging, Santa Clara, CA 95054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Buchanan","sequence":"additional","affiliation":[{"name":"Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2B7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5314-2297","authenticated-orcid":false,"given":"Jacob L.","family":"Jaremko","sequence":"additional","affiliation":[{"name":"Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dornoosh","family":"Zonoobi","sequence":"additional","affiliation":[{"name":"Exo Imaging, Santa Clara, CA 95054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arun","family":"Nagdev","sequence":"additional","affiliation":[{"name":"Exo Imaging, Santa Clara, CA 95054, USA"},{"name":"Alameda Health System, Highland Hospital, University of California San Francisco, San Francisco, CA 94143, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeevesh","family":"Kapur","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University of Singapore, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1164\/rccm.201802-0236CI","article-title":"Lung Ultrasound for Critically Ill Patients","volume":"199","author":"Mojoli","year":"2019","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1111\/anae.15082","article-title":"Point-of-care lung ultrasound in patients with COVID-19\u2014A narrative review","volume":"75","author":"Smith","year":"2020","journal-title":"Anaesthesia"},{"key":"ref_3","first-page":"41","article-title":"Implementation of lung ultrasound in polyvalent intensive care unit: Impact on irradiation and medical cost","volume":"34","author":"Zieleskiewicz","year":"2015","journal-title":"Anaesth. Crit. Care Pain. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e200564","DOI":"10.1148\/ryct.2021200564","article-title":"Lung Ultrasound: The Essentials","volume":"3","author":"Marini","year":"2021","journal-title":"Radiol. Cardiothorac. Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1186\/cc5668","article-title":"Clinical review: Bedside lung ultrasound in critical care practice","volume":"11","author":"Bouhemad","year":"2007","journal-title":"Crit. Care"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s13089-023-00356-z","article-title":"Advancement in pleura effusion diagnosis: A systematic review and meta-analysis of point-of-care ultrasound versus radiographic thoracic imaging","volume":"16","author":"Zaki","year":"2024","journal-title":"Ultrasound J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13089-017-0059-y","article-title":"Systematic review and meta-analysis for the use of ultrasound versus radiology in diagnosing of pneumonia","volume":"9","author":"Alzahrani","year":"2017","journal-title":"Crit. Ultrasound J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e190703","DOI":"10.1001\/jamanetworkopen.2019.0703","article-title":"Diagnostic Accuracy of Point-of-Care Lung Ultrasonography and Chest Radiography in Adults with Symptoms Suggestive of Acute Decompensated Heart Failure: A Systematic Review and Meta-analysis","volume":"2","author":"Maw","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1002\/ppul.24020","article-title":"Lung ultrasound compared to chest X-ray for diagnosis of pediatric pneumonia: A meta-analysis","volume":"53","author":"Balk","year":"2018","journal-title":"Pediatr. Pulmonol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/1465-9921-15-50","article-title":"Lung ultrasound for the diagnosis of pneumonia in adults: A systematic review and meta-analysis","volume":"15","author":"Chavez","year":"2014","journal-title":"Respir. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1378\/chest.07-2800","article-title":"Relevance of lung ultrasound in the diagnosis of acute respiratory failure: The BLUE protocol","volume":"134","author":"Lichtenstein","year":"2008","journal-title":"Chest"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1378\/chest.14-1313","article-title":"BLUE-protocol and FALLS-protocol: Two applications of lung ultrasound in the critically ill","volume":"147","author":"Lichtenstein","year":"2015","journal-title":"Chest"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1186\/s13054-017-1897-5","article-title":"Thoracic ultrasound for pleural effusion in the intensive care unit: A narrative review from diagnosis to treatment","volume":"21","author":"Brogi","year":"2017","journal-title":"Crit. Care"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1097\/00000542-200401000-00006","article-title":"Comparative diagnostic performances of auscultation, chest radiography, and lung ultrasonography in acute respiratory distress syndrome","volume":"100","author":"Lichtenstein","year":"2004","journal-title":"Anesthesiology"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Benson, R.E.C. (2019). Lung Cancer: Atelectasis and Consolidation In Chest Imaging, Oxford University Press.","DOI":"10.1093\/med\/9780199858064.003.0047"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13089-015-0020-x","article-title":"The risk of cumulative radiation exposure in chest imaging and the advantage of bedside ultrasound","volume":"7","author":"Gargani","year":"2015","journal-title":"Crit. Ultrasound J."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hong, D., Choi, H., Hong, W., Kim, Y., Kim, T.J., Choi, J., Ko, S.B., and Park, C.M. (2024). Deep-learning model accurately classifies multi-label lung ultrasound findings, enhancing diagnostic accuracy and inter-reader agreement. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-72484-y"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e14231","DOI":"10.1002\/acm2.14231","article-title":"Automated detection and segmentation of pleural effusion on ultrasound images using an Attention U-net","volume":"25","author":"Huang","year":"2024","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vukovic, D., Wang, A., Antico, M., Steffens, M., Ruvinov, I., van Sloun, R.J., van Sloun, R.J., Canty, D., Royse, A., and Royse, C. (2023). Automatic deep learning-based pleural effusion segmentation in lung ultrasound images. BMC Med. Inf. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02362-6"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1001\/jamacardio.2024.4991","article-title":"Artificial Intelligence-Guided Lung Ultrasound by Nonexperts","volume":"10","author":"Baloescu","year":"2025","journal-title":"JAMA Cardiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"240263","DOI":"10.1183\/16000617.0263-2024","article-title":"Advanced imaging techniques and artificial intelligence in pleural diseases: A narrative review","volume":"34","author":"Marchi","year":"2025","journal-title":"Eur. Respir. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Arntfield, R., Wu, D., Tschirhart, J., VanBerlo, B., Ford, A., Ho, J., McCauley, J., Wu, B., Deglint, J., and Chaudhary, R. (2021). Automation of lung ultrasound interpretation via deep learning for the classification of normal versus abnormal lung parenchyma: A multicenter study. Diagnostics, 11.","DOI":"10.3390\/diagnostics11112049"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100687","DOI":"10.1016\/j.imu.2021.100687","article-title":"Automated detection of pneumonia in lung ultrasound using deep video classification for COVID-19","volume":"25","author":"Krishnaswamy","year":"2021","journal-title":"Inf. Med. Unlocked"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110066","DOI":"10.1016\/j.ejrad.2021.110066","article-title":"Performance of a computer aided diagnosis system for SARS-CoV-2 pneumonia based on ultrasound images","volume":"146","author":"Shang","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.ajem.2025.01.041","article-title":"Diagnostic accuracy of an automated classifier for the detection of pleural effusions in patients undergoing lung ultrasound","volume":"90","author":"Chaudhary","year":"2025","journal-title":"Am. J. Emerg. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"127317","DOI":"10.1016\/j.neucom.2024.127317","article-title":"Automated detection and forecasting of COVID-19 using deep learning techniques: A review","volume":"577","author":"Shoeibi","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2398","DOI":"10.1016\/j.ultrasmedbio.2022.07.007","article-title":"State of the art in lung ultrasound, shifting from qualitative to quantitative analyses","volume":"48","author":"Mento","year":"2022","journal-title":"Ultrasound Med. Biol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"031006","DOI":"10.1117\/1.JMI.1.3.031006","article-title":"Generalized Roe and Metz receiver operating characteristic model: Analytic link between simulated decision scores and empirical AUC variances and covariances","volume":"1","author":"Gallas","year":"2014","journal-title":"J. Med. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 10: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_31","unstructured":"Signorell, A. (2025, June 03). DescTools: Tools for Descriptive Statistics and Exploratory Data Analysis. Available online: https:\/\/github.com\/AndriSignorell\/DescTools."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"92","DOI":"10.25080\/Majora-92bf1922-011","article-title":"Statsmodels: Econometric and Statistical Modeling with Python","volume":"7","author":"Seabold","year":"2010","journal-title":"SciPy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102125","DOI":"10.1016\/j.media.2021.102125","article-title":"Deep learning for chest X-ray analysis: A survey","volume":"72","author":"Sogancioglu","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1109\/TUFFC.2020.3002249","article-title":"Automated Lung Ultrasound B-Line Assessment Using a Deep Learning Algorithm","volume":"67","author":"Baloescu","year":"2020","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Durrani, N., Vukovic, D., van der Burgt, J., Antico, M., van Sloun, R.J.G., Canty, D., Steffens, M., Wang, A., Royse, A., and Royse, C. (2022). Automatic deep learning-based consolidation\/collapse classification in lung ultrasound images for COVID-19 induced pneumonia. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-22196-y"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e230860","DOI":"10.1148\/radiol.230860","article-title":"Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs","volume":"309","author":"Bennani","year":"2023","journal-title":"Radiology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.ajem.2025.03.029","article-title":"A scoping review on the integration of artificial intelligence in point-of-care ultrasound: Current clinical applications","volume":"92","author":"Kim","year":"2025","journal-title":"Am. J. Emerg. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1186\/s13054-023-04548-w","article-title":"Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit","volume":"27","author":"Nhat","year":"2023","journal-title":"Crit. Care"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ejmp.2021.02.023","article-title":"Automatic deep learning-based pleural effusion classification in lung ultrasound images for respiratory pathology diagnosis","volume":"83","author":"Tsai","year":"2021","journal-title":"Phys. Med."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Born, J., Wiedemann, N., Cossio, M., Buhre, C., Br\u00e4ndle, G., Leidermann, K., Goulet, J., Aujayeb, A., Moor, M., and Rieck, B. (2021). Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis. Appl. Sci., 11.","DOI":"10.3390\/app11020672"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1097\/CCM.0000000000005759","article-title":"Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU","volume":"51","author":"Dave","year":"2023","journal-title":"Crit. Care Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.chest.2024.02.011","article-title":"Automated Real-Time Detection of Lung Sliding Using Artificial Intelligence: A Prospective Diagnostic Accuracy Study","volume":"166","author":"Prager","year":"2024","journal-title":"Chest"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1001\/jamacardio.2021.0185","article-title":"Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use","volume":"6","author":"Narang","year":"2021","journal-title":"JAMA Cardiol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/17\/2145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:32:22Z","timestamp":1760034742000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/17\/2145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,25]]},"references-count":43,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["diagnostics15172145"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15172145","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,25]]}}}