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

SCH：新生儿面部编码疼痛识别监测系统 (PRAMS)

• 批准号: 2205472
• 负责人: RENEE MANWORREN
• 金额: $ 119.98万
• 依托单位: Ann & Robert H. Lurie Children's Hospital of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2205472&HistoricalAwards=false
• 关键词: SCH; Neonatal; Facial; Coding; Pain

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.

疼痛每年影响超过1500万住院的婴儿。早期疼痛与异常的结构和功能性脑发育有关，并导致不良后果，包括认知障碍，情绪功能改变，精神病理学和全球疼痛敏感性。使用与大脑基于疼痛的证据相关的面部表情，护士只同意在67-87％的时间出现婴儿的疼痛。这是无法自我报告疼痛的，该模型在分析一小部分婴儿的分析中提供了94％的精度，93％的精度和95％的回忆。该模型不够强大，无法部署进行连续疼痛评估，直到可以用大量的潜水婴儿样本完全开发出来。该项目正在整合到研究人员提供的教育活动中，包括基于联合学习（FL）概念和算法的首个大规模开放在线课程。这项研究计划的目的是提高以生物学证据为疼痛和由护士循环监督的生物学证据的自动疼痛识别AI能力监测系统（PRAM）。通过使用潜水员疼痛评估数据集对一种新型的混合方法进行测试，该数据集是由时间序列的面部动作视频，八个患者护理单位手术前后200多名婴儿的物理和临床数据创建的；因此，模拟院间分布式学习。数学证据表明，这种新型混合FL方法在凸学习问题中具有有利的收敛特征，以在将来建立相似的收敛范围，以进行非凸优化。 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 precious of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.