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

• URL: http://arxiv.org/abs/2311.00565v2
• Date: Fri, 12 Jul 2024 15:05:24 GMT
• Title: Detecting Visual Cues in the Intensive Care Unit and Association with Patient Clinical Status
• Authors: Subhash Nerella, Ziyuan Guan, Andrea Davidson, Yuanfang Ren, Tezcan Baslanti, Brooke Armfield, Patrick Tighe, Azra Bihorac, Parisa Rashidi,
• Abstract summary: Existing patient assessments in the ICU are mostly sporadic and administered manually. We developed a new "masked loss computation" technique that addresses the data imbalance problem. We performed AU inference on 634,054 frames to evaluate the association between facial AUs and clinically important patient conditions.
• Score: 0.9867627975175174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intensive Care Units (ICU) provide close supervision and continuous care to patients with life-threatening conditions. However, continuous patient assessment in the ICU is still limited due to time constraints and the workload on healthcare providers. Existing patient assessments in the ICU such as pain or mobility assessment are mostly sporadic and administered manually, thus introducing the potential for human errors. Developing Artificial intelligence (AI) tools that can augment human assessments in the ICU can be beneficial for providing more objective and granular monitoring capabilities. For example, capturing the variations in a patient's facial cues related to pain or agitation can help in adjusting pain-related medications or detecting agitation-inducing conditions such as delirium. Additionally, subtle changes in visual cues during or prior to adverse clinical events could potentially aid in continuous patient monitoring when combined with high-resolution physiological signals and Electronic Health Record (EHR) data. In this paper, we examined the association between visual cues and patient condition including acuity status, acute brain dysfunction, and pain. We leveraged our AU-ICU dataset with 107,064 frames collected in the ICU annotated with facial action units (AUs) labels by trained annotators. We developed a new "masked loss computation" technique that addresses the data imbalance problem by maximizing data resource utilization. We trained the model using our AU-ICU dataset in conjunction with three external datasets to detect 18 AUs. The SWIN Transformer model achieved 0.57 mean F1-score and 0.89 mean accuracy on the test set. Additionally, we performed AU inference on 634,054 frames to evaluate the association between facial AUs and clinically important patient conditions such as acuity status, acute brain dysfunction, and pain.

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