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Intensive Care Units (ICU) provide close supervision and continuous care topatients with life-threatening conditions. However, continuous patientassessment in the ICU is still limited due to time constraints and the workloadon healthcare providers. Existing patient assessments in the ICU such as painor mobility assessment are mostly sporadic and administered manually, thusintroducing the potential for human errors. Developing Artificial intelligence(AI) tools that can augment human assessments in the ICU can be beneficial forproviding more objective and granular monitoring capabilities. For example,capturing the variations in a patient's facial cues related to pain oragitation can help in adjusting pain-related medications or detectingagitation-inducing conditions such as delirium. Additionally, subtle changes invisual cues during or prior to adverse clinical events could potentially aid incontinuous patient monitoring when combined with high-resolution physiologicalsignals and Electronic Health Record (EHR) data. In this paper, we examined theassociation between visual cues and patient condition including acuity status,acute brain dysfunction, and pain. We leveraged our AU-ICU dataset with 107,064frames collected in the ICU annotated with facial action units (AUs) labels bytrained annotators. We developed a new "masked loss computation" technique thataddresses the data imbalance problem by maximizing data resource utilization.We trained the model using our AU-ICU dataset in conjunction with threeexternal datasets to detect 18 AUs. The SWIN Transformer model achieved 0.57mean F1-score and 0.89 mean accuracy on the test set. Additionally, weperformed AU inference on 634,054 frames to evaluate the association betweenfacial AUs and clinically important patient conditions such as acuity status,acute brain dysfunction, and pain.

Detecting Visual Cues in the Intensive Care Unit and Association with Patient Clinical Status