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:10579988
- 负责人:
- 金额:$ 37.61万
- 依托单位:
- 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:
- 关键词:Acetic AcidsAdoptedAdoptionAnalgesicsAnimal ModelBiological AssayBody RegionsCellular PhoneCharacteristicsClassificationColorColor blindnessComputer softwareComputersCustomDataData AnalyticsDatabase Management SystemsDependenceDevelopmentFaceFacial ExpressionFacial PainFailureFutureGenesGoalsHumanHuman ResourcesImageLaparotomyManualsMapsMeasuresMedicineMethodsModalityModelingMotionMusNeural Network SimulationNon-Steroidal Anti-Inflammatory AgentsOpioid AnalgesicsOutputPainPain MeasurementPain intensityPharmaceutical PreparationsPostoperative PainPre-Clinical ModelPublishingReproducibilityResearch PersonnelResourcesRodentScienceScreening procedureSleepSpecificitySystemTestingTimeTrainingaddictionanalytical toolassociated symptomchronic paincloud basedconvolutional neural networkdata repositorydeep neural networkefficacy evaluationhandheld mobile deviceimprovedinflammatory paininterestmachine learning algorithmmachine learning modelmobile applicationmultimodalityneural circuitnovelopen sourceopioid epidemicoverdose deathpain reliefpainful neuropathypersistent symptompre-clinicalrecurrent neural networkside effectsmartphone applicationspontaneous painsuccesstooltransfer learninguser-friendlyweb platformweb 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.
项目总结
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Mark J. Zylka其他文献
The environmental neuroactive chemicals list of prioritized substances for human biomonitoring and neurotoxicity testing: A database and high-throughput toxicokinetics approach
- DOI:
10.1016/j.envres.2024.120537 - 发表时间:
2025-02-01 - 期刊:
- 影响因子:
- 作者:
Julia E. Rager;Lauren E. Koval;Elise Hickman;Caroline Ring;Taylor Teitelbaum;Todd Cohen;Giulia Fragola;Mark J. Zylka;Lawrence S. Engel;Kun Lu;Stephanie M. Engel - 通讯作者:
Stephanie M. Engel
Correction to: Chd8 haploinsufficiency impairs early brain development and protein homeostasis later in life
- DOI:
10.1186/s13229-021-00438-6 - 发表时间:
2021-05-08 - 期刊:
- 影响因子:5.500
- 作者:
Jessica A. Jiménez;Travis S. Ptacek;Alex H. Tuttle;Ralf S. Schmid;Sheryl S. Moy;Jeremy M. Simon;Mark J. Zylka - 通讯作者:
Mark J. Zylka
Mark J. Zylka的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Mark J. Zylka', 18)}}的其他基金
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 - 财政年份:2020
- 资助金额:
$ 37.61万 - 项目类别:
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 - 财政年份:2020
- 资助金额:
$ 37.61万 - 项目类别:
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 - 财政年份:2020
- 资助金额:
$ 37.61万 - 项目类别:
CRISPR/Cas9-based gene therapy for Angelman syndrome
基于 CRISPR/Cas9 的 Angelman 综合征基因疗法
- 批准号:
10490828 - 财政年份:2019
- 资助金额:
$ 37.61万 - 项目类别:
Environmental-use chemicals that target pathways linked to autism and other neurodevelopmental disorders
针对与自闭症和其他神经发育障碍相关途径的环境使用化学品
- 批准号:
10402265 - 财政年份:2019
- 资助金额:
$ 37.61万 - 项目类别:
CRISPR/Cas9-based gene therapy for Angelman syndrome
基于 CRISPR/Cas9 的 Angelman 综合征基因疗法
- 批准号:
10237150 - 财政年份:2019
- 资助金额:
$ 37.61万 - 项目类别:
Environmental-use chemicals that target pathways linked to autism and other neurodevelopmental disorders
针对与自闭症和其他神经发育障碍相关途径的环境使用化学品
- 批准号:
10618242 - 财政年份:2019
- 资助金额:
$ 37.61万 - 项目类别:
CRISPR/Cas9-based gene therapy for Angelman syndrome
基于 CRISPR/Cas9 的 Angelman 综合征基因疗法
- 批准号:
10011898 - 财政年份:2019
- 资助金额:
$ 37.61万 - 项目类别:
Identification of candidate environmental risks for autism
识别自闭症的候选环境风险
- 批准号:
9525549 - 财政年份:2017
- 资助金额:
$ 37.61万 - 项目类别:
Lipid kinase regulation of pain signaling and sensitization
脂质激酶对疼痛信号传导和敏化的调节
- 批准号:
9279273 - 财政年份:2013
- 资助金额:
$ 37.61万 - 项目类别:
相似海外基金
How novices write code: discovering best practices and how they can be adopted
新手如何编写代码:发现最佳实践以及如何采用它们
- 批准号:
2315783 - 财政年份:2023
- 资助金额:
$ 37.61万 - 项目类别:
Standard Grant
One or Several Mothers: The Adopted Child as Critical and Clinical Subject
一位或多位母亲:收养的孩子作为关键和临床对象
- 批准号:
2719534 - 财政年份:2022
- 资助金额:
$ 37.61万 - 项目类别:
Studentship
A material investigation of the ceramic shards excavated from the Omuro Ninsei kiln site: Production techniques adopted by Nonomura Ninsei.
对大室仁清窑遗址出土的陶瓷碎片进行材质调查:野野村仁清采用的生产技术。
- 批准号:
20K01113 - 财政年份:2020
- 资助金额:
$ 37.61万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
A comparative study of disabled children and their adopted maternal figures in French and English Romantic Literature
英法浪漫主义文学中残疾儿童及其收养母亲形象的比较研究
- 批准号:
2633211 - 财政年份:2020
- 资助金额:
$ 37.61万 - 项目类别:
Studentship
A comparative study of disabled children and their adopted maternal figures in French and English Romantic Literature
英法浪漫主义文学中残疾儿童及其收养母亲形象的比较研究
- 批准号:
2436895 - 财政年份:2020
- 资助金额:
$ 37.61万 - 项目类别:
Studentship
A comparative study of disabled children and their adopted maternal figures in French and English Romantic Literature
英法浪漫主义文学中残疾儿童及其收养母亲形象的比较研究
- 批准号:
2633207 - 财政年份:2020
- 资助金额:
$ 37.61万 - 项目类别:
Studentship
A Study on Mutual Funds Adopted for Individual Defined Contribution Pension Plans
个人设定缴存养老金计划采用共同基金的研究
- 批准号:
19K01745 - 财政年份:2019
- 资助金额:
$ 37.61万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
The limits of development: State structural policy, comparing systems adopted in two European mountain regions (1945-1989)
发展的限制:国家结构政策,比较欧洲两个山区采用的制度(1945-1989)
- 批准号:
426559561 - 财政年份:2019
- 资助金额:
$ 37.61万 - 项目类别:
Research Grants
Securing a Sense of Safety for Adopted Children in Middle Childhood
确保被收养儿童的中期安全感
- 批准号:
2236701 - 财政年份:2019
- 资助金额:
$ 37.61万 - 项目类别:
Studentship
Structural and functional analyses of a bacterial protein translocation domain that has adopted diverse pathogenic effector functions within host cells
对宿主细胞内采用多种致病效应功能的细菌蛋白易位结构域进行结构和功能分析
- 批准号:
415543446 - 财政年份:2019
- 资助金额:
$ 37.61万 - 项目类别:
Research Fellowships