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

ABSTRACTSO_ST_ABSOBJECTIVEC_ST_ABSThe 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. DESIGNDiagnostic/prognostic study SETTINGPre-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. PARTICIPANTSA 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 MEASURESThe 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. RESULTSA 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 [&ge;]2, adjusted HR 3.73 [95% CI, 2.09-6.64], reference z-score <-1). CONCLUSIONSFully 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. What is already known on this topic?Previous deep learning research in the diagnosis of cardiovascular disease using chest X-rays has focused on predicting specific disease categories, forecasting cardiovascular outcomes, and automatically measuring the cardiothoracic (CT) ratio. The end-to-end learning methods that predict disease categories or outcomes are typically limited to specific conditions and often lack explainability. While the CT ratio is traditionally used in chest X-ray analysis, it often lacks well-defined normal ranges and may not effectively detect conditions such as aortic dilatation or pulmonary trunk enlargement. What this study addsTo the best of our knowledge, this is the first study to propose age- and sex-specific normal values for all cardiovascular borders (CVBs) as well as the CT ratio. Utilizing 96,129 normal chest X-rays from multiple centers, we have established normal ranges for CVBs and standardized these values into z-score mapping. This approach simplifies and enhances the practicality of clinical application. The z-score mapping of CVBs has demonstrated clinical utility in diagnosing and categorizing diseases, as well as in predicting prognosis. The AI software that automatically analyzes CVBs from CXR is available for external validation and free trial use through our dedicated research website (www.adcstudy.com). This study has transformed the interpretation of cardiovascular configuration on chest X-ray from subjective expert assessments to objective, quantifiable, and standardized measurements expressed as z-scores.