Development of a deep neural network to measure spontaneous pain from mouse facial expressions

开发深度神经网络来测量小鼠面部表情的自发疼痛

基本信息

批准号：
10349447
负责人：
Mark J. Zylka
金额：
$ 37.61万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-15 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10349447
关键词：
Acetic Acids Adopted Adoption Analgesics Animal Model Biological Assay Body Regions Cellular Phone Characteristics Classification Color Computer software Computers Custom Data Data Analytics Database Management Systems Dependence Development Face Facial Expression Facial Pain Failure Future Genes Goals Human Human Resources Image Laparotomy Manuals Maps Measures Medicine Methods Modality Modeling Motion Mus Neural Network Simulation Non-Steroidal Anti-Inflammatory Agents Online Systems Operative Surgical Procedures Opioid Analgesics Output Pain Pain Measurement Pain intensity Pharmaceutical Preparations Postoperative Pain Pre-Clinical Model Publishing Reflex action Reproducibility Research Personnel Resources Rodent Science Screening procedure Sleep Specificity Supervision System Testing Time Training addiction analytical tool associated symptom base blind chronic pain chronic pain relief cloud based convolutional neural network data repository deep neural network efficacy evaluation handheld mobile device improved inflammatory pain interest machine learning algorithm machine learning model mobile application multimodality neural circuit novel open source opioid epidemic overdose death pain relief painful neuropathy persistent symptom pre-clinical recurrent neural network side effect smartphone Application spontaneous pain success tool transfer learning user-friendly web services

项目摘要

PROJECT SUMMARY Opioid analgesics are commonly used to treat pain but have serious side effects, including addiction, dependence, and death from overdose. While there is a significant need for new non-addictive analgesics, efforts to develop new pain medicines have met with limited success. In part, this failure is due to an overreliance on evoked pain measures in preclinical models. Indeed, most preclinical models do not measure spontaneous pain—the main symptom of chronic pain in humans. To increase translational relevance, the Mouse Grimace Scale (MGS) was developed to quantify characteristic facial expressions associated with spontaneous pain. The MGS is reproducible across labs and was used to evaluate the efficacy of analgesics. However, the MGS has not been widely adopted due to its high resource demands and low throughput. To overcome this limitation, we adapted a machine learning model to classify the presence or absence of pain from mouse facial expressions. We called this model the automated Mouse Grimace Scale (aMGS). After training, this model identified mice in pain with 94% accuracy, comparable to a highly-trained human. However, our original “aMGS 1.0” is limited in several respects. It is only accurate at detecting facial grimacing in white- coated mice, and produces a binary assessment (“pain” vs. “no pain”) instead of a graded score. Moreover, aMGS 1.0 cannot dynamically determine pain status from full-motion videos. Additionally, we relied on an older piece of software that does not consistently extract high-quality images of the mouse face. The aMGS 1.0 also has difficulty distinguishing between images of sleeping and grimacing mice. Finally, aMGS 1.0 suffers from a “black box” problem inherent to most machine learning algorithms, in that we do not know what facial details it uses to produce a pain assessment. Here we propose to overcome all of these limitations by developing a more sophisticated version of our automated pain classifier (aMGS 2.0). To achieve this goal we will: 1) Develop and validate a new open-source platform to classify (frame-by-frame) spontaneous pain intensity from mouse facial expressions, using albino (white) mice and motion information. 2) Enhance the generality of aMGS 2.0 for use with black mice. And, 3) Develop a user-friendly web-based platform that operates on computer-based and mobile devices. We will validate the utility of aMGS with three pain assays that produce grimaces in rodents—inflammatory pain, post-surgical (laparotomy) pain, and neuropathic pain. To increase rigor and reproducibility, two pain assays will be performed and scored with aMGS 2.0 in an independent lab. Numerous investigators in the pain field have expressed interest in using our proposed model. The platform will include a cloud-based data repository and analytic tools to facilitate curation of public data, continuous improvement of the model over time, and integration of new analytic tools. One analytic tool that we plan to develop will identify mouse features that most influence pain classification.