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.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01NR013500-03
Application #
8688812
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Marden, Susan F
Project Start
Project End
Budget Start
Budget End
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California San Diego
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Sikka, Karan; Dhall, Abhinav; Bartlett, Marian Stewart (2014) Classification and Weakly Supervised Pain Localization using Multiple Segment Representation. Image Vis Comput 32:659-670