Keywords: Machine Learning/Artificial Intelligence, Heart, Deep learning segmentation, cardiomegalyDespite its ubiquity, cardiac assessment at non-cardiac-gated MRI has yet to be standardized, leading to misdiagnosis. This paper examines the feasibility of AI for automatic 3D volumetric cardiac quantification used to detect and stratify cardiomegaly on non-cardiac-gated torso MRI. Using AI, we automatically measured the cardiothoracic ratio and 3D cardiac volumetric features as indicators for detections of cardiomegaly conditions. Large-scale results on 3485 normal-heart individuals revealed the effect of aging on cardiac features in both men and women. AI findings on non-cardiac-gated imaging offer opportunistic useful information, increasing the diagnostic precision, and allowing potentially useful imaging monitoring for cardiomegaly-associated conditions.
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