More Than Meets the Eye: Analyzing Anesthesiologists' Visual Attention in the Operating Room Using Deep Learning Models
This addresses the problem of unsustainable wearable eye-tracking for large-scale data collection in operating rooms, offering a less intrusive method for monitoring anesthesiologists' visual behavior to potentially improve patient safety, though it is incremental as it builds on existing eye-tracking concepts.
The study tackled the challenge of analyzing anesthesiologists' visual attention in operating rooms by developing a deep learning model using monitor-mounted webcams, which collected continuous behavioral data with minimal workflow disruption and distinguished visual patterns between uneventful periods and critical incidents.
Patient's vital signs, which are displayed on monitors, make the anesthesiologist's visual attention (VA) a key component in the safe management of patients under general anesthesia; moreover, the distribution of said VA and the ability to acquire specific cues throughout the anesthetic, may have a direct impact on patient's outcome. Currently, most studies employ wearable eye-tracking technologies to analyze anesthesiologists' visual patterns. Albeit being able to produce meticulous data, wearable devices are not a sustainable solution for large-scale or long-term use for data collection in the operating room (OR). Thus, by utilizing a novel eye-tracking method in the form of deep learning models that process monitor-mounted webcams, we collected continuous behavioral data and gained insight into the anesthesiologist's VA distribution with minimal disturbance to their natural workflow. In this study, we collected OR video recordings using the proposed framework and compared different visual behavioral patterns. We distinguished between baseline VA distribution during uneventful periods to patterns associated with active phases or during critical, unanticipated incidents. In the future, such a platform may serve as a crucial component of context-aware assistive technologies in the OR.