This study investigates the use of deep learning to detect left ventricular (LV) structural abnormalities, specifically severe LV hypertrophy (SLVH) and dilated left ventricle (DLV), from chest X-rays (CXRs). The researchers developed a deep learning model that inputs a pre-processed CXR, age, and sex to output probabilities for SLVH, DLV, and a composite label indicating the presence of either structural abnormality. The model outperformed 15 board-certified radiologists in detecting these abnormalities, achieving higher sensitivity at the same specificity. The model's performance was consistent across different datasets from Columbia University Irving Medical Center and Stanford University Medical Center. Saliency maps revealed that the model is sensitive to the cardiac silhouette and areas around the left ventricle. The study demonstrates the potential of deep learning as a tool for early detection of LV structural abnormalities, which could lead to earlier diagnosis and improved outcomes for patients with heart failure.This study investigates the use of deep learning to detect left ventricular (LV) structural abnormalities, specifically severe LV hypertrophy (SLVH) and dilated left ventricle (DLV), from chest X-rays (CXRs). The researchers developed a deep learning model that inputs a pre-processed CXR, age, and sex to output probabilities for SLVH, DLV, and a composite label indicating the presence of either structural abnormality. The model outperformed 15 board-certified radiologists in detecting these abnormalities, achieving higher sensitivity at the same specificity. The model's performance was consistent across different datasets from Columbia University Irving Medical Center and Stanford University Medical Center. Saliency maps revealed that the model is sensitive to the cardiac silhouette and areas around the left ventricle. The study demonstrates the potential of deep learning as a tool for early detection of LV structural abnormalities, which could lead to earlier diagnosis and improved outcomes for patients with heart failure.