Development of a new technology for assessing pediatric pain (NTAP)

开发评估儿科疼痛的新技术 (NTAP)

• 批准号: 8554320
• 负责人: MARIAN Stewart BARTLETT
• 金额: $ 47.82万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-30 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8554320
• 关键词: Abdominal Pain; Address; Adoption; Algorithms; Appendectomy; Behavior; Behavioral; Child; Child health care; Childhood; Clinical; Clinical Research; Clinical assessments; Cohort Studies; Communication; Computer Vision Systems; Data; Data Set; Detection; Development; Emerging Technologies; Evaluation; Face; Facial Expression; Facial Expression Recognition; Foundations; Funding; Future; Goals; Health; Health Benefit; Healthcare; Hospitalized Child; In Situ; Intervention; Lead; Length of Stay; Machine Learning; Measurement; Measures; Medical; Methods; Monitor; Morbidity - disease rate; Operating System; Operative Surgical Procedures; Outcome; Pain; Pain Measurement; Pain intensity; Pain management; Pancreatitis; Parents; Patient Self-Report; Patients; Pattern Recognition; Pharmaceutical Preparations; Physiological; Physiology; Population; Populations at Risk; Postoperative Pain; Protocols documentation; Proxy; Psychometrics; Qualifying; Reproducibility; Research; Research Personnel; Rights; Sampling; Severities; Signal Transduction; Standardization; System; Target Populations; Techniques; Technology; Testing; Time; Training; Validity and Reliability; Wireless Technology; Work; World Health Organization; acute pancreatitis; base; detector; experience; health organization; improved; mortality; new technology; novel; patient population; prototype; sensor; tool

DESCRIPTION (provided by applicant): Advanced sensing and pattern recognition technologies open new possibilities for automated clinical assessment. Integration of this technology into the clinical arena is thus timely. In particular, there is promise in the use of suh technologies to provide automated assessment of poorly quantifiable clinical variables such as pain. Suboptimal pain assessment is particularly prevalent in children, who often rely on pain assessment by proxy which has been shown repeatedly to poorly correlate with patients' self-reports of pain. A number of observational scales have been developed for assessing pain by proxy. However, even some of the most widely used clinical scales were not developed from a rigorous psychometric perspective. Characterizations of the facial display in pain differ dramatically from each other, and differ substantially from empirical descriptions, leading to dramatically different estimates of pain. Suboptimal pain assessment in children results in delays in adequate pain management and unrelieved pain, which may contribute to significant morbidity and mortality in children. Recognition of this issue has led the World Health Organization to mandate that health entities recognize the rights of children to have their pain alleviated. In order to accomplish this goal, a more reliable and accurate method for pain assessment in this at-risk population is needed. We propose the Development of a Novel Tool for the Assessment of Pediatric Pain (NTAP). The primary aim is to develop and evaluate an automated NTAP tool that utilizes novel computer vision and wearable physiology sensor technologies to estimate pain severity in children. The research team comprises expertise from researchers in computer vision (Bartlett & Littlewort), pediatric clinical research and child healt outcomes (Huang), physiological measurement (el Kaliouby & Picard), and pain assessment in children (Craig). The project will collect a dataset of clinical pain in children following a known pain insult (pancreatitis, and postoperative pain following appendectomy.) The dataset will contain video, electrodermal signals, self-report of pain intensity, elapsed time since pain insult and clinical severity ratings. Initial analysis of collected video data will be performed using our NSF-funded automated facial expression recognition system (CERT: Bartlett & Littlewort), and electrodermal activity (EDA) monitoring and recording will be performed by the wearable, wireless Q Sensor from Affectiva (el Kaliouby & Picard). Machine learning (the development of algorithms for making predictions based on a large set of examples/data) will be employed to develop a system for estimating pain from facial expression and electrodermal activity signals. Evaluation protocols will address validity, reliability, and reproducibility. The proposed NTAP too will provide an automated pain estimation system for pediatric pain in the clinical setting that may improve pain assessment in children and provide a foundation for pain assessment in populations with communication limitations.

描述（由申请人提供）：先进的传感和模式识别技术为自动化临床评估开辟了新的可能性。因此，将该技术整合到临床竞技场是及时的。特别是，有希望在使用suh技术，以提供自动化评估难以量化的临床变量，如疼痛。次优的疼痛评估在儿童中特别普遍，他们经常依赖于代理的疼痛评估，这已经反复显示与患者自我报告的疼痛相关性很差。已经开发了许多观察量表用于通过替代评估疼痛。然而，即使是一些最广泛使用的临床量表也不是从严格的心理测量学角度开发的。疼痛时面部表情的特征彼此差异很大，与经验描述有很大不同，导致对疼痛的估计差异很大。儿童疼痛评估不佳导致疼痛管理延迟和疼痛未缓解，这可能导致儿童发病率和死亡率显著增加。世界卫生组织认识到这一问题，因此要求卫生机构承认儿童减轻痛苦的权利。为了实现这一目标，需要一种更可靠和准确的方法来评估这一高危人群的疼痛。我们建议开发一种新的工具，用于评估儿科疼痛（NTAP）。主要目的是开发和评估一种自动化NTAP工具，该工具利用新型计算机视觉和可穿戴生理传感器技术来估计儿童疼痛的严重程度。该研究团队包括计算机视觉（Bartlett & Littlewort），儿科临床研究和儿童健康结果（Huang），生理测量（el Kaliouby & Picard）和儿童疼痛评估（克雷格）研究人员的专业知识。该项目将收集一个已知的儿童临床疼痛数据集， 疼痛损伤（胰腺炎和阑尾切除术后疼痛。）该数据集将包含视频、皮肤电信号、疼痛强度的自我报告、自疼痛损伤以来的经过时间和临床严重程度评级。对收集的视频数据的初步分析将使用我们的 NSF资助的自动面部表情识别系统（CERT：Bartlett & Littlewort）和皮肤电活动（EDA）监测和记录将由Affectiva（el Kaliouby & Picard）的可穿戴无线Q传感器进行。机器学习（开发基于大量示例/数据进行预测的算法）将用于开发一个从面部表情和皮肤电活动信号估计疼痛的系统。评价方案将涉及有效性、可靠性和再现性。拟议的NTAP也将为临床环境中的儿科疼痛提供一个自动疼痛评估系统，该系统可能会改善儿童疼痛评估，并为沟通受限人群的疼痛评估提供基础。