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: 10717670
• 负责人: Mark J. Zylka
• 金额: $ 3.97万
• 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/10717670
• 关键词: Acetic Acids; Adopted; Adoption; Analgesics; Animal Model; Biological Assay; Body Regions; Cellular Phone; Characteristics; Classification; Color; Color blindness; 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; Opioid Analgesics; Output; Pain; Pain Measurement; Pain intensity; Pharmaceutical Preparations; Postoperative Pain; Pre-Clinical Model; Publishing; Reproducibility; Research Personnel; Resources; Rodent; Science; Screening procedure; Sleep; Specificity; System; Testing; Time; Training; addiction; analytical tool; associated symptom; chronic pain; 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 platform; web services

ORIGINAL APPLICATION 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.

原始申请项目摘要 阿片类镇痛药通常用于治疗疼痛，但具有严重的副作用，包括成瘾、 尽管迫切需要新的非成瘾性镇痛药， 开发新止痛药的努力取得了有限的成功，部分原因是由于 临床前模型中过度依赖诱发疼痛测量 事实上，大多数临床前模型不测量。 自发性疼痛——人类慢性疼痛的主要症状。 小鼠鬼脸量表 (MGS) 的开发是为了量化与以下行为相关的特征性面部表情： MGS 可在实验室中重现，并用于评估镇痛药的功效。 然而，MGS由于其高资源需求和低吞吐量而没有被广泛采用。 为了克服这个限制，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 我们将此模型称为自动小鼠鬼脸量表 (aMGS)。 经过训练，该模型识别小鼠疼痛的准确率高达 94%，与训练有素的人类相当。 我们最初的“aMGS 1.0”在几个方面都有局限性，它只能准确地检测白色的面部表情。 涂覆小鼠，并产生二元评估（“疼痛”与“无疼痛”）而​​不是分级分数。 aMGS 1.0 无法动态确定全动态视频的疼痛状态此外，我们依赖于较旧的版本。 aMGS 1.0 也不能始终提取高质量的鼠标面部图像。 难以区分正在睡觉的老鼠和做鬼脸的老鼠的图像最后，aMGS 1.0 还存在一个问题。 大多数机器学习算法固有的“黑匣子”问题，因为我们不知道它的面部细节 在这里，我们建议通过开发来克服所有这些限制。 我们的自动疼痛分类器的更复杂版本（aMGS 2.0）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，用于对自发疼痛强度进行分类（逐帧） 小鼠面部表情，使用白化小鼠和运动信息 2）增强通用性。 aMGS 2.0 用于黑鼠，并且，3) 开发一个用户友好的基于网络的平台。 我们将通过三种疼痛检测来验证 aMGS 的实用性。 啮齿类动物的鬼脸——炎性疼痛、手术后（剖腹手术）疼痛和神经性疼痛。 为了保证严谨性和可重复性，将在独立实验室中使用 aMGS 2.0 进行两次疼痛测定并进行评分。 疼痛领域的许多研究人员都表示有兴趣使用我们提出的模型。 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、持续 随着时间的推移模型的改进，以及我们计划集成的新分析工具。 开发将识别对疼痛分类影响最大的小鼠特征。