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

• 批准号: 10094266
• 负责人: Mark J. Zylka
• 金额: $ 36.22万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 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/10094266
• 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; Machine Learning; 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; Psychological Transfer; 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; 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; user-friendly; web services

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.

项目总结 阿片类镇痛剂通常用于治疗疼痛，但有严重的副作用，包括成瘾， 依赖，以及过量服药导致的死亡。虽然对新的非成瘾性镇痛剂的需求很大， 开发新止痛药的努力取得了有限的成功。在一定程度上，这种失败是由于 临床前模型中对诱发疼痛措施的过度依赖。事实上，大多数临床前模型并不能衡量 自发性疼痛--人类慢性疼痛的主要症状。为了提高翻译相关性， 开发了老鼠表情量表(MGS)来量化与以下内容相关的特征面部表情 自发性疼痛。MGS可在多个实验室重复使用，并用于评估止痛药的疗效。 然而，由于MGS对资源的高需求和低吞吐量，MGS还没有被广泛采用。至 克服了这一限制，我们采用了机器学习模型来对疼痛的存在或不存在进行分类 从老鼠的面部表情。我们称这种模型为自动老鼠鬼脸秤(AMGS)。之后 经过训练，该模型识别疼痛小鼠的准确率为94%，可与训练有素的人类相媲美。然而， 我们最初的“AMGS 1.0”在几个方面都受到了限制。它只在检测白色面部鬼脸时才准确- 并产生一个二元评估(“疼痛”和“没有疼痛”)，而不是一个分级的分数。此外， AMGS 1.0不能从全动态视频中动态确定疼痛状态。此外，我们还依赖于一台更老的 一款不能持续提取高质量的鼠标面部图像的软件。AMGS 1.0还 很难区分睡着的老鼠和做鬼脸的老鼠。最后，AMGS 1.0遭受了 “黑盒”问题是大多数机器学习算法固有的问题，因为我们不知道它的面部细节 用来进行疼痛评估。在这里，我们建议通过开发 我们的自动疼痛分类器(AMGS 2.0)的更复杂版本。为了实现这一目标，我们将：1) 开发并验证一个新的开源平台来对自发性疼痛强度进行(逐帧)分类 老鼠面部表情，使用白化(白色)老鼠和运动信息。2)增强通用性 适用于黑鼠的AMGS 2.0。以及，3)开发用户友好的基于Web的平台，在 基于计算机的和移动设备。我们将通过三种疼痛测试来验证AMG的实用性 啮齿动物的鬼脸--炎症性疼痛、手术(剖腹)后疼痛和神经病理性疼痛。增加 为了提高疼痛的严密性和可重复性，将在一个独立的实验室进行两项疼痛分析，并使用AMGS 2.0进行评分。 许多疼痛领域的研究人员都表示有兴趣使用我们提出的模型。站台 将包括基于云的数据存储库和分析工具，以促进公共数据的管理 随着时间的推移对模型进行改进，并集成新的分析工具。我们计划使用的一个分析工具 开发人员将识别对疼痛分类影响最大的鼠标特征。