SCH: Neonatal Facial Coding for Pain Recognition Monitoring System (PRAMS)

每年有超过1500万名住院婴儿受到疼痛的影响。早期疼痛与大脑结构和功能发育异常有关，并会导致不良后果，包括认知障碍、情绪功能改变、精神病理和全局疼痛敏感性。使用与基于大脑的疼痛证据相关的面部表情，护士只在67%-87%的时间内同意婴儿疼痛的存在。因此，无法自我报告疼痛会使婴儿容易受到疼痛治疗不足和过度治疗的影响。研究人员创建并验证了儿科护士基于新生儿疼痛视频数据集的面部动作建立的初步人工智能(AI)授权的疼痛分类模型。在对小样本婴儿的分析中，该模型提供了94%的准确率、93%的准确率和95%的召回率。这个模型还不够强大，不能用于持续的疼痛评估，直到它可以在不同婴儿的大样本中完全开发出来。该项目正在融入调查人员提供的教育活动，包括第一个基于联合学习(FL)概念和算法的大规模在线开放课程。这项研究计划的目标是推动创建一个自动疼痛识别人工智能授权的监控系统(PRMS)，该系统以疼痛的生物证据为基础，并由现场护士监督。一种新的混合FL方法正在通过使用不同的疼痛评估数据集进行测试，该数据集是从8个患者护理单元的200多名婴儿的手术前后的时间序列面部动作视频、生理和临床数据创建的；因此，模拟医院间的分布式学习。数学证明了这种新的混合FL方法在凸学习问题中具有良好的收敛特性，以便在未来建立类似的非凸优化的收敛界。这个项目具有巨大的潜力，可以在隐私保护的FL方法中推动跨不同数据集的机器学习算法的开发，该方法可以利用多站点数据的统计能力来学习即使是罕见疾病的临床有意义的特征。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Pain affects over 15 million hospitalized babies annually. Early-life pain is associated with abnormal structural and functional brain development and results in adverse consequences, including cognitive impairments, altered emotional functioning, psychopathologies, and global pain sensitivity. Using facial expressions associated with brain-based evidence of pain, nurses only agree to the presence of babies’ pain 67-87% of the time. Thus, the inability to self-report pain makes babies vulnerable to under- and over-treatment of pain. The investigators created and pediatric nurses validated, a preliminary artificial intelligence (AI)-empowered pain classification model based on facial actions from a video dataset of newborn pain. This model provides 94% accuracy, 93% precision, and 95% recall in analyses of a small sample of babies. This model is not robust enough to be deployed for continuous pain assessment until it can be fully developed with a large sample of diverse babies. This project is being integrated into educational activities offered by the investigators, including the first massive open online course based on federated learning (FL) concepts and algorithms. The goal of this program of research is to advance the creation of an automated Pain Recognition AI-empowered Monitoring System (PRAMS) grounded by biological evidence of pain and supervised by nurses-in-the loop. A novel hybrid FL approach is being tested by using a diverse pain assessment dataset that is being created from time-series facial action video, physiological and clinical data of more than 200 babies before and after surgery in eight patient care units; thus, simulating inter-hospital distributed learning. Mathematical proof that this novel hybrid FL approach has advantageous convergence characteristics in convex learning problems is being provided to establish in the future similar convergence bounds for non-convex optimization. This project has great potential to advance the development of machine learning algorithms across heterogeneous datasets in a privacy-preserving FL approach that could leverage the statistical power of multi-site data to learn clinically meaningful features of even rare conditions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.