Deep learning to detect left ventricular structural abnormalities in chest X-rays
Open accessAbstract
Background and Aims: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs. Methods: A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists. Results: The model yielded an AUROC of 0.79 (0.76–0.81) for SLVH, 0.80 (0.77–0.84) for DLV, and 0.80 (0.78–0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%. Conclusions: Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available.
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This method of human-machine comparison is similar to typical methods and one we have used previously.
“This method of human–machine comparison is similar to typical methods and one we have used previously34,35.”
From Detecting structural heart disease from electrocardiograms using AI (2025) · cites this paper as [35]
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The false hope of current approaches to explainable artificial intelligence in health care
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“However, there has been limited work on building methods for detecting cardiac pathology with CXRs. Identification of incidental cardiac abnormalities in patients undergoing a CXR may allow for earlier recognition and treatment of cardiac disease.”
BackgroundInfluentialVideo-based AI for beat-to-beat assessment of cardiac function
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Sorted by citations
Detecting structural heart disease from electrocardiograms using AI
Poterucha, T. J., Jing, L., Ricart, R. P., Adjei-Mosi, M., Finer, J., Hartzel, D., Kelsey, C., Long, A., Rocha, D., Ruhl, J. A., et al. (2025) · Nature · doi:10.1038/s41586-025-09227-0 · Cited by 116 · see citing works
Self-citationExplainable Artificial Intelligence in Radiological Cardiovascular Imaging—A Systematic Review
Haupt, M., Maurer, M. H., & Thomas, R. P. (2025) · Diagnostics · doi:10.3390/diagnostics15111399 · Cited by 58 · see citing works
Self-citation
- Authors
- Bhave, S., Rodriguez, V., Poterucha, T., Mutasa, S., Aberle, D., Capaccione, K. M., Chen, Y., Dsouza, B., Dumeer, S., Goldstein, J., Hodes, A., Leb, J., Lungren, M., Miller, M., Monoky, D., Navot, B., Wattamwar, K., Wattamwar, A., Clerkin, K., Ouyang, D., Ashley, E., Topkara, V. K., Maurer, M., Einstein, A. J., Uriel, N., Homma, S., Schwartz, A., Jaramillo, D., Perotte, A. J., & Elias, P.
- Affiliations
- Columbia University Irving Medical Center (US)
- NewYork–Presbyterian Hospital (US)
- Inova Fairfax Hospital (US)
- Department of Radiology, Columbia University Irving Medical Center , NewYork, NY , USA
- Hackensack Meridian School of Medicine (US)
- University of California, San Francisco (US)
- Montefiore Medical Center (US)
- Cedars-Sinai Medical Center (US)
- Stanford Medicine (US)
- Corresponding author
- Elias, Pierre
- ORCID
- Bhave, Shreyas
- Poterucha, Timothy
- Mutasa, Simukayi
- Capaccione, Kathleen M
- Chen, Yibo
- Dumeer, Shifali
- Goldstein, Jonathan
- Hodes, Aaron
- Leb, Jay
- Lungren, Matthew
- Navot, Benjamin
- Wattamwar, Kapil
- Clerkin, Kevin
- Ouyang, David
- Ashley, Euan
- Topkara, Veli K
- Maurer, Mathew
- Einstein, Andrew J
- Uriel, Nir
- Schwartz, Allan
- Jaramillo, Diego
- Perotte, Adler J
- Elias, Pierre
- Venue
- European Heart Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-03-20
- Volume
- 45
- Issue
- 22
- Pages
- 2002-2012
- ISSN
- 0195-668X (ISSN-L)
- 1522-9645
- PMC
- PMC11156488
- OpenAlex
- W4394741028
- Cited by
- 38
- References
- 6
- Citations by year
- 2026: 13 · 2025: 17 · 2024: 8
- FWCI
- 9.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Heart Journal (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- left ventricular hypertrophy, left ventricular dilatation, heart failure, chest radiography, deep learning, cardiac structural abnormalities, echocardiography, radiologist comparison, early detection, public datasets
- MeSH terms
- Deep Learning, Aged, Echocardiography, Female, Heart Failure, Heart Ventricles, Humans, Male, Middle Aged, ROC Curve, Radiography, Thoracic, Hypertrophy, Left Ventricular
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