Applicability of deep learning for blood pressure estimation during hemodialysis based on facial images

Abstract

Abstract Back ground In hemodialysis, hypotension occurs due to dehydration and solute removal. Conventional blood pressure monitoring during dialysis is intermittent and relies on staff experience and intuition to predict patient blood pressure trends based on the amount of water removed on the day and previous trends, which requires hemodialysis operations that do not lead to hypotension. Our research group has attempted to estimate blood pressure based on the spatial features of facial visible images, including information on facial color, and facial infrared images, including information on skin temperature. It is expected to realize early detection of blood pressure decrease during treatment if the blood pressure of dialysis patients can be estimated from their facial visible and infrared images measured continuously and remotely. In this study, we verified the applicability of deep learning algorithms in blood pressure estimation based on facial visible and infrared images of hemodialysis patients. Methods Measured facial visible and infrared images and mean blood pressure (MBP) of hemodialysis patients were applied to a convolutional neural network to construct an MBP estimation model based on the spatial features of the facial images. Results Average blood pressure could be estimated with an error of less than 20 mmHg based on the spatial features of the facial images, and the blood pressure estimation accuracy based on the spatial features of the facial infrared images was higher than that of the facial visible images. Conclusion We found the possibility of applying the deep learning algorithm to blood pressure estimation based on the spatial features of facial images. Trial registration This study is not subject to enrollment in a clinical trial due to the absence of both intervention and invasion. The Ethics Review Committee of Jichi Medical University has approved the same interpretation.