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化临床评估打开了新的可能性。因此，将这项技术整合到临床领域是及时的。特别是，使用SuH技术提供对疼痛等难以量化的临床变量的自动评估是有希望的。次优疼痛评估在儿童中尤其普遍，他们经常依赖代理疼痛评估，这已被反复证明与患者对疼痛的自我报告相关性很差。已经开发了一些观察量表来通过代理来评估疼痛。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。对疼痛中面部表现的描述彼此之间有很大的不同，并且与经验描述有很大的不同，导致对疼痛的估计有很大的不同。对儿童进行不理想的疼痛评估会导致延误适当的疼痛管理和无法缓解的疼痛，这可能会导致儿童的严重发病率和死亡率。由于认识到这一问题，世界卫生组织要求卫生实体承认儿童有权减轻他们的痛苦。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。我们建议开发一种新的儿科疼痛评估工具(NTAP)。主要目的是开发和评估一种自动化的NTAP工具，该工具利用新的计算机视觉和可穿戴生理传感器技术来评估儿童疼痛的严重程度。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局(Huang)、生理测量(el Kaliouby&Picard)和儿童疼痛评估(Craig)等领域的研究人员的专业知识。该项目将收集儿童临床疼痛的数据集，此前已知 疼痛侮辱(胰腺炎和阑尾切除术后的疼痛。)该数据集将包含视频、皮肤电信号、疼痛强度的自我报告、自疼痛侮辱以来经过的时间和临床严重程度评级。对收集到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(开发基于大量实例/数据进行预测的算法)将被用于开发一种根据面部表情和皮肤电活动信号估计疼痛的系统。评估方案将涉及有效性、可靠性和可重复性。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的疼痛评估，并为沟通受限人群的疼痛评估提供基础。

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.