Detection of Left Ventricular Systolic Dysfunction Using an Artificial Intelligence-Enabled Chest X-Ray

Background: Assessment of left ventricular systolic dysfunction provides essential information related to the prognosis and management of cardiovascular diseases. The aim of this study was to develop a deep-learning model to identify left ventricular ejection fraction (LVEF) <= 35% via chest X-ray (CXR [CXR-EF <= 35%]) features and investigate the performance and clinical implications. Methods: This study collected 90,547 CXRs with the corresponding LVEF according to transthoracic echocardiography from the outpatient department in an academic medical center. Among these, 77,227 CXRs were used to develop the identification of CXR-EF <= 35%. Another 13,320 CXRs were used to validate the performance, which was evaluated by area under the receiver operating characteristic curve (AUC). Furthermore, CXR-EF <= 35% was tested to assess the long-term risks of developing LVEF <= 35% and cardiovascular outcomes, which were evaluated by Kaplan-Meier survival analysis and the Cox proportional hazards model. Results: The AUCs of CXR-EF <= 35% for the detection of LVEF <= 35% were 0.888 and 0.867 in the internal and external validation cohorts, respectively. Patients with baseline LVEF > 50% but detected as CXREF <= 35% were at higher risk of long-term development of LVEF <= 35% (hazard ratio, internal validation cohort [HRi] 3.91, 95% CI 2.98-5.14; hazard ratio, external validation cohort [HRe] 2.49, 95% CI 1.89-3.27). Furthermore, patients detected as LVEF <= 35% by CXR-EF <= 35% had significantly higher future risks of all-cause mortality (HRi 1.40, 95% CI 1.15-1.71; HRe 1.38, 95% CI 1.15-1.66), cardiovascular mortality (HRi 3.02, 95% CI 1.84-4.98; HRe 2.60, 95% CI 1.77-3.82), and new-onset atrial fibrillation (HRi 2.81, 95% CI 2.15-3.66; HRe 2.93, 95% CI 2.34-3.67) compared with those detected as no LVEF <= 35%. Conclusions: CXR-EF <= 35% may serve as a screening tool for early detection of LVEF <= 35% and could independently contribute to predictions of long-term development of LVEF <= 35% and cardiovascular outcomes. Further prospective studies are needed to confirm the model performance.

Automated summary

The study developed a deep-learning model to identify left ventricular ejection fraction (LVEF) <= 35% via chest X-ray images, which demonstrated excellent performance in internal and external validation and could potentially be used for early prediction of cardiovascular disease development.