Early Prediction of Hemodynamic Shock in the Intensive Care Units with Deep Learning on Thermal Videos

Abstract Shock is one of the major killers in Intensive Care Units and early interventions can potentially reverse it. In this study, we advance a non-contact thermal imaging modality to continuous monitoring of hemodynamic shock working on 103,936 frames from 406 videos recorded longitudinally upon 22 patients. Deep learning was used to preprocess and extract the Center-to-Peripheral Difference (CPD) in temperature values from the videos. This time-series data along with heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 hours. Our models achieved the best area under the receiver operating characteristics curve of 0.81 ± 0.06 and area under the precision-recall curve of 0.78 ± 0.05 at 5 hours, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable shock prediction using an automated decision pipeline, that can provide better care and save lives.