Automated, standardized, quantitative analysis of cardiovascular borders on chest X-rays using deep learning for assessing cardiovascular disease

OBJECTIVE The analysis of cardiovascular borders (CVBs) on chest X-rays (CXRs) has traditionally relied on subjective assessment, and the cardiothoracic (CT) ratio, its sole quantitative marker, does not reflect great vessel changes and lacks established normal ranges. This study aimed to develop a deep learning-based method for quantifying CVBs on CXRs and to explore its clinical utility. DESIGN Diagnostic/prognostic study SETTING Pre-validated deep learning for quantification and z-score standardization of CVBs: the superior vena cava/ascending aorta (SVC/AO), right atrium (RA), aortic arch, pulmonary artery, left atrial appendage (LAA), left ventricle (LV), descending aorta, and carinal angle. PARTICIPANTS A total of 96,129 normal CXRs from 4 sites were used to establish age- and sex-specific normal ranges of CVBs. The clinical utility of the z-score analysis was tested using 44,567 diseased CXRs from 3 sites. MAIN OUTCOMES MEASURES The area under the curve (AUC) for detecting disease, differences in z-scores for classifying subtypes, and hazard ratio (HR) for predicting 5-year risk of death or myocardial infarction. RESULTS: A total of 44,567 patients with disease (9964 valve disease; 32,900 coronary artery disease; 1299 congenital heart disease; 294 aortic aneurysm; 110 mediastinal mass) were analyzed. For distinguishing valve disease from normal controls, the AUC for the CT ratio was 0.79 (95% CI, 0.78-0.80), while the combination of RA and LV had an AUC of 0.82 (95% CI, 0.82-0.83). Between mitral and aortic stenosis, z-scores of CVBs were significantly different in LAA (1.54 vs. 0.33, p<0.001), carinal angle (1.10 vs. 0.67, p<0.001), and SVC/AO (0.63 vs. 1.02, p<0.001), reflecting distinct disease pathophysiology (dilatation of LA vs. AO). CT ratio was independently associated with a 5-year risk of death or myocardial infarction in the coronary artery disease group (z-score ≥2, adjusted HR 3.73 [95% CI, 2.09-6.64], reference z-score <-1). CONCLUSIONS Fully automated, deep learning-derived z-score analysis of CXR showed potential in detecting, classifying, and stratifying the risk of cardiovascular abnormalities. Further research is needed to determine the most beneficial clinical scenarios for this method.