Radiol Cardiothorac Imaging. 2026 Aug;8(4):e250411. doi: 10.1148/ryct.250411.
ABSTRACT
Purpose To develop a deep learning (DL) algorithm for identification of cardiac chamber enlargement (CCE) on anteroposterior chest radiographs using same-day transthoracic echocardiography (TTE) as a reference standard. Materials and Methods Between January 2019 and December 2021, anteroposterior chest radiographs obtained within 24 hours of TTE were retrospectively collected and randomly assigned to training (n = 5158), validation (n = 655), and test (n = 654) sets. A pretrained EfficientNet-B6 model was adapted to predict the presence of any CCE and enlargement of individual cardiac chambers. Model performance was compared with manual cardiothoracic ratio (CTR) measurements on the test set and with assessments by three cardiothoracic radiologists on a subset of 200 test set chest radiographs. Results A total of 6467 anteroposterior chest radiographs from unique patients (mean age, 63.4 years ± 17.0 [SD]; 3820 [59.1%] male patients) were included, with CCE present in 4060 (62.8%) cases. For binary classification of CCE, the model achieved an area under the receiver operating characteristic curve (AUC) value of 0.80 in the validation set and 0.83 in the test set. Corresponding performance in the validation and test sets, respectively, was as follows: accuracy, 74% and 76%; sensitivity, 76% and 81%; specificity, 71% and 66%; and area under the precision-recall curve value, 0.87 and 0.89. On the test set, the model outperformed cardiothoracic ratio (CTR) measurements (AUC, 0.83 vs 0.74) and cardiothoracic radiologist assessment (accuracy, 77% vs 60.5%-67.5%; all P < .001). Conclusion In this proof-of-concept study, a DL model demonstrated robust performance for detection of CCE on anteroposterior chest radiographs and outperformed CTR measurements and cardiothoracic radiologist assessment in a controlled evaluation setting. Keywords: Deep Learning, Algorithm Development, Neural Networks, Echocardiography, Conventional Radiography, Cardiac, Data Science, Machine Learning, Mass Chest X-Ray, Transthoracic Echocardiography, Radiographic Image Interpretation-Computer-Assisted, Cardiomegaly Supplemental material is available for this article. © RSNA, 2026.
PMID:42390349 | DOI:10.1148/ryct.250411