  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.
