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

每年有超过1500万住院婴儿受到疼痛的影响。生命早期疼痛与大脑结构和功能发育异常有关，并导致不良后果，包括认知障碍、情绪功能改变、精神病理和整体疼痛敏感性。使用与大脑疼痛证据相关的面部表情，护士只在67-87%的情况下同意婴儿疼痛的存在。因此，无法自我报告疼痛使婴儿容易受到疼痛治疗不足和过度的伤害。研究人员创建了一个初步的人工智能（AI）疼痛分类模型，该模型基于新生儿疼痛视频数据集中的面部动作，儿科护士对此进行了验证。该模型在小样本婴儿的分析中提供了94%的准确度，93%的精度和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，直到它可以完全发展与不同的婴儿的大样本。该项目被整合到研究者提供的教育活动中，包括第一个基于联邦学习（FL）概念和算法的大规模开放在线课程。该研究计划的目标是推进基于疼痛生物学证据并由护士监督的自动疼痛识别人工智能监测系统（PRAMS）的创建。一种新型的混合FL方法正在使用不同的疼痛评估数据集进行测试，该数据集是由8个病人护理单位的200多名婴儿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，从而为非凸优化建立了相似的收敛界。该项目具有很大的潜力，可以通过保护隐私的FL方法推进跨异构数据集的机器学习算法的发展，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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.