Detecting and Reversing Myocardial Ischemia Using an Artificially Intelligent Bioelectronic Medicine

Summary Myocardial ischemia is spontaneous, usually asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, biological neural networks cannot reliably detect and correct myocardial ischemia on their own. In this study, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements biological neural networks enabling reliable detection and correction of myocardial ischemia. ANNs were first trained to decode spontaneous cardiovascular stress and myocardial ischemia with an overall accuracy of ∼92%. ANN-controlled vagus nerve stimulation (VNS) reversed the major biomarkers of myocardial ischemia with no side effects. In contrast, open-loop VNS or ANN-controlled VNS following a caudal vagotomy essentially failed to reverse correlates of myocardial ischemia. Lastly, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations and unsupervised detection of emerging cardiovascular stress states. Overall, these results demonstrate that ANNs can supplement deficient biological neural networks via an artificially intelligent bioelectronic medicine system. ### Competing Interest Statement The authors have declared no competing interest.