Embodiments described herein provide various examples of monitoring adverse events in the background while displaying a higher-resolution surgical video on a lower-resolution display device. In one aspect, a process for detecting adverse events during a surgical procedure can begin by receiving a surgical video. The process then displays a first portion of the video images of the surgical video on a screen to assist a surgeon performing the surgical procedure. While displaying the first portion of the video images, the process uses a set of deep-learning models to monitor a second portion of the video images not being displayed on the screen, wherein each deep-learning model is constructed to detect a given adverse event among a set of adverse events. In response to detecting an adverse event in the second portion of the video images, the process notifies the surgeon of the detected adverse event to prompt an appropriate action.
