Discovery of cardiac imaging biomarkers by training neural network models across diagnostic modalities

Machines can be readily trained to automate medical image interpretation, with the primary goal of replicating human capabilities. Here, we propose an alternative role: using machine learning to discover pragmatic imaging-based biomarkers by interpreting one complex imaging modality via a second, more ubiquitous, lower-cost modality. We applied this strategy to train convolutional neural network models to estimate positron emission tomography (PET)-derived myocardial blood flow (MBF) at rest and with hyperemic stress, and their ratio, coronary flow reserve (CFR), using contemporaneous two-dimensional echocardiography videos as inputs. The resulting parameters, echoAI-restMBF, echoAI-stressMBF, and echoAI-CFR modestly approximated the original values. However, using echocardiograms of 5,393 (derivation) and 5,289 (external validation) patients, we show they sharply stratify individuals according to disease comorbidities and combined with baseline demographics, are strong predictors for heart failure hospitalization (C-statistic derivation: 0.79, 95% confidence interval 0.77-0.81; validation: 0.81, 0.79-0.82) and acute coronary syndrome (C-statistic derivation: 0.77, 0.73-0.80; validation: 0.75, 0.73-0.78). Using echocardiograms of 3,926 genotyped individuals, we estimate narrow-sense heritability of 9.2%, 20.4% and 6.5%, respectively for echoAI-restMBF, echoAI-stressMBF, and echoAI-CFR. MBF indices show inverse genetic correlation with impedance-derived body mass indices, such as fat-free body mass (e.g., {rho}=-0.43, q=0.05 for echoAI-restMBF) and resolve conflicting historical data regarding body mass index and CFR. In terms of diseases, genetic association with ischemic heart disease is seen most prominently for echoAI-stressMBF ({rho}=-0.37, q=2.4x10-03). We hypothesize that interpreting one imaging modality through another represents a type of "information bottleneck", capturing latent features of the original physiologic measurements that have relevance across tissues. Thus, we propose a broader potential role for machine learning algorithms in developing scalable biomarkers that are anchored in known physiology, representative of latent biological factors, and are readily deployable in population health applications.