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: 8439693
• 负责人: MARIAN Stewart BARTLETT
• 金额: $ 60.47万
• 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/8439693
• 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. PUBLIC HEALTH RELEVANCE: Suboptimal pain assessment in children is unfortunately common and results in unrelieved pain, and untreated pain contributes to significant morbidity and mortality in children. The World Health Organization and other health organizations have mandated 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. Emerging technologies with their high potential for standardization and reproducibility, as well as grounding in empirical data through machine learning, are uniquely qualified for clinical pain assessment. We will develop and test an automated tool that utilizes novel computer vision and wearable physiology sensor technologies to estimate pain severity in children.

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领域是及时的。特别是，使用 SUH 技术有望对疼痛等难以量化的临床变量进行自动评估。次优的疼痛评估在儿童中尤其普遍，他们经常依赖代理疼痛评估，这一评估已被反复证明与患者自我报告的疼痛相关性较差。已经开发了许多观察量表来通过代理评估疼痛。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角度开发的。疼痛时面部表现的特征彼此之间存在显着差异，并且与经验描述也存在显着差异，从而导致对疼痛的估计显着不同。儿童疼痛评估不理想会导致适当疼痛管理的延迟和疼痛无法缓解，这可能会导致儿童显着的发病率和死亡率。认识到这一问题后，世界卫生组织要求卫生实体承认儿童有减轻疼痛的权利。为了实现这一目标，需要一种更可靠、更准确的方法来评估这一高危人群的疼痛。我们建议开发一种用于评估儿科疼痛（NTAP）的新工具。主要目的是开发和评估自动化 NTAP 工具，该工具利用新颖的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。该研究团队由计算机视觉（Bartlett & Littlewort）、儿科临床研究和儿童健康结果（Huang）、生理测量（el Kaliouby & Picard）以及儿童疼痛评估（Craig）等领域的研究人员组成。该项目将按照已知的方法收集儿童临床疼痛的数据集 疼痛侮辱（胰腺炎和阑尾切除术后疼痛）。数据集将包含视频、皮肤电信号、疼痛强度的自我报告、疼痛侮辱后经过的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）以及皮肤电活动 (EDA) 监测和记录将由 Affectiva (el Kaliouby & Picard) 的可穿戴无线 Q 传感器执行。机器学习（开发基于大量示例/数据进行预测的算法）将用于开发一个根据面部表情和皮肤电活动信号估计疼痛的系统。评估方案将解决有效性、可靠性和再现性问题。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，该系统可以改善儿童的疼痛评估，并为沟通受限人群的疼痛评估奠定基础。 公共卫生相关性：不幸的是，儿童疼痛评估不理想的情况很常见，会导致疼痛无法缓解，而未经治疗的疼痛会导致儿童显着的发病率和死亡率。世界卫生组织和其他卫生组织已要求卫生实体承认儿童获得减轻疼痛的权利；为了实现这一目标，需要一种更可靠、更准确的方法来评估这一高危人群的疼痛。新兴技术具有高度标准化和可重复性潜力，并且通过机器学习以经验数据为基础，因此具有独特的临床疼痛评估资格。我们将开发和测试一种自动化工具，利用新颖的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。