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

描述（由申请人提供）：先进的传感和模式识别技术为自动临床评估开辟了新的可能性。因此，将这项技术整合到临床领域是及时的。特别是，在使用这些技术为难以量化的临床变量（如疼痛）提供自动评估方面，有很大的希望。次优疼痛评估在儿童中尤其普遍，他们通常依赖于通过代理进行疼痛评估，这与患者自我报告的疼痛之间的相关性很差。许多观察量表已经被开发出来用于评估疼痛。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。疼痛时面部表现的特征彼此之间差异很大，与经验描述也有很大差异，导致对疼痛的估计差异很大。儿童疼痛评估不理想导致适当疼痛管理和疼痛未缓解的延迟，这可能导致儿童的显著发病率和死亡率。认识到这一问题，世界卫生组织责成卫生实体承认儿童有减轻痛苦的权利。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些高危人群的疼痛。我们建议开发一种评估儿童疼痛的新工具（NTAP）。主要目的是开发和评估一种自动化的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。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）和儿童疼痛评估（Craig）的研究人员组成。该项目将收集一个已知的儿童临床疼痛数据集

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.