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: 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
• 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

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， 依赖和过量的死亡。虽然非常需要新的非添加性镇痛药，但 开发新止痛药的努力取得了有限的成功。部分原因是 临床前模型中诱发疼痛度量的过度依赖。确实，大多数临床前模型都无法测量 赞助疼痛 - 人类慢性疼痛的主要症状。为了增加转化相关性， 开发了小鼠鬼脸量表（MGS），以量化与 赞助疼痛。 MGS在实验室之间可再现，并用于评估镇痛药的效率。 但是，由于MGS的资源较高和吞吐量较低，因此MGS并未被广泛采用。到 克服这一限制，我们调整了机器学习模型以对存在或不存在疼痛进行分类 从小鼠面部表情。我们称此模型为自动鼠标鬼脸量表（AMGS）。后 训练，该模型确定了疼痛的小鼠精度为94％，与受过训练的人相当。然而， 我们最初的“ AMGS 1.0”在几个方面受到限制。它仅准确地检测白色的面部鬼脸 涂层小鼠，并产生二进制评估（“疼痛”与“无疼痛”），而不是分级分数。而且， AMGS 1.0无法通过全动作视频动态确定疼痛状态。此外，我们依靠较老的 不会始终提取鼠标脸的高质量图像的软件。 AMGS 1.0也 在睡眠和酸痛的小鼠的图像之间很难区分。最后，AMGS 1.0遭受 “黑匣子”问题继承了大多数机器学习算法，因为我们不知道面部细节 用于产生疼痛评估的用途。在这里，我们建议通过开发来克服所有这些限制 我们自动疼痛分类器（AMGS 2.0）的更复杂的版本。为了实现这一目标，我们将：1） 从 小鼠面部表情，使用白化病（白色）小鼠和运动信息。 2）提高一般性 AMGS 2.0与黑鼠一起使用。 3）开发一个基于用户友好的网络平台，可在 基于计算机和移动设备。我们将使用三个产生的疼痛测定法验证AMG的实用性 啮齿动物中的鬼魂 - 炎症性疼痛，术后（腹腔切开术）疼痛和神经性疼痛。增加 严格和可重复性，将在独立实验室中对AMGS 2.0进行两次疼痛测定。 疼痛场的许多研究人员对使用我们提出的模型表示兴趣。平台 将包括一个基于云的数据存储库和分析工具，以促进公共数据的策划，继续 随着时间的推移改进模型，并集成新的分析工具。我们计划的一种分析工具 开发将确定最大程度影响疼痛分类的鼠标特征。