重症监护室中视觉线索的检测及其与患者临床状态的关联
Detecting Visual Cues in the Intensive Care Unit and Association with Patient Clinical Status
AI总结：
本研究针对ICU患者评估零散且人工易出错的问题，提出掩码损失计算技术，结合多数据集训练SWIN Transformer模型检测18种面部动作单元，并验证了其与患者病情严重程度、急性脑功能障碍及疼痛的临床关联。
摘要：
重症监护室（ICU）为患有危及生命疾病的患者提供密切监护与持续护理。然而，由于时间限制以及医护人员的工作负担，ICU中的持续患者评估仍然存在局限。现有的ICU患者评估（如疼痛或活动能力评估）大多是零散开展且由人工实施的，因此存在人为错误的可能性。开发能够辅助ICU中人工评估的人工智能（AI）工具，有助于提供更客观、更精细的监测能力。例如，捕捉患者与疼痛或躁动相关的面部线索变化，可帮助调整疼痛相关药物或检测诱发躁动的病症（如谵妄）。此外，不良临床事件发生期间或之前视觉线索的细微变化，若与高分辨率生理信号及电子健康记录（EHR）数据相结合，有望助力持续患者监测。在本文中，我们研究了视觉线索与患者状态（包括病情严重程度、急性脑功能障碍和疼痛）之间的关联。我们利用了自主构建的AU-ICU数据集，该数据集包含在ICU中采集的107064帧图像，由经过培训的标注人员标注了面部动作单元（AUs）标签。我们提出了一种新的“掩码损失计算”技术，通过最大化数据资源利用率来解决数据不平衡问题。我们结合AU-ICU数据集与三个外部数据集训练模型，以检测18种面部动作单元。SWIN Transformer模型在测试集上取得了0.57的平均F1分数和0.89的平均准确率。此外，我们对634054帧图像进行了面部动作单元推理，以评估面部动作单元与临床上重要的患者状态（如病情严重程度、急性脑功能障碍和疼痛）之间的关联。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
