Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors
The automation of behavioral tracking and analysis in preclinical research can serve to advance the rate of research outcomes, increase experimental scalability, and challenge the scientific reproducibility crisis. Recent advances in the efficiency, accuracy, and accessibility of deep learning (DL)...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-09-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.953182/full |
| _version_ | 1811204292677206016 |
|---|---|
| author | Jacob R. Bumgarner Darius D. Becker-Krail Rhett C. White Randy J. Nelson |
| author_facet | Jacob R. Bumgarner Darius D. Becker-Krail Rhett C. White Randy J. Nelson |
| author_sort | Jacob R. Bumgarner |
| collection | DOAJ |
| description | The automation of behavioral tracking and analysis in preclinical research can serve to advance the rate of research outcomes, increase experimental scalability, and challenge the scientific reproducibility crisis. Recent advances in the efficiency, accuracy, and accessibility of deep learning (DL) and machine learning (ML) frameworks are enabling this automation. As the ongoing opioid epidemic continues to worsen alongside increasing rates of chronic pain, there are ever-growing needs to understand opioid use disorders (OUDs) and identify non-opioid therapeutic options for pain. In this review, we examine how these related needs can be advanced by the development and validation of DL and ML resources for automated pain and withdrawal behavioral tracking. We aim to emphasize the utility of these tools for automated behavioral analysis, and we argue that currently developed models should be deployed to address novel questions in the fields of pain and OUD research. |
| first_indexed | 2024-04-12T03:10:35Z |
| format | Article |
| id | doaj.art-2ed1dba916aa4a7c871ae5f060d1951e |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-12T03:10:35Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-2ed1dba916aa4a7c871ae5f060d1951e2022-12-22T03:50:21ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-09-011610.3389/fnins.2022.953182953182Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviorsJacob R. BumgarnerDarius D. Becker-KrailRhett C. WhiteRandy J. NelsonThe automation of behavioral tracking and analysis in preclinical research can serve to advance the rate of research outcomes, increase experimental scalability, and challenge the scientific reproducibility crisis. Recent advances in the efficiency, accuracy, and accessibility of deep learning (DL) and machine learning (ML) frameworks are enabling this automation. As the ongoing opioid epidemic continues to worsen alongside increasing rates of chronic pain, there are ever-growing needs to understand opioid use disorders (OUDs) and identify non-opioid therapeutic options for pain. In this review, we examine how these related needs can be advanced by the development and validation of DL and ML resources for automated pain and withdrawal behavioral tracking. We aim to emphasize the utility of these tools for automated behavioral analysis, and we argue that currently developed models should be deployed to address novel questions in the fields of pain and OUD research.https://www.frontiersin.org/articles/10.3389/fnins.2022.953182/fullpainopioid withdrawalopioid use disorder (OUD)deep learningmachine learningmarkerless tracking |
| spellingShingle | Jacob R. Bumgarner Darius D. Becker-Krail Rhett C. White Randy J. Nelson Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors Frontiers in Neuroscience pain opioid withdrawal opioid use disorder (OUD) deep learning machine learning markerless tracking |
| title | Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors |
| title_full | Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors |
| title_fullStr | Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors |
| title_full_unstemmed | Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors |
| title_short | Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors |
| title_sort | machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors |
| topic | pain opioid withdrawal opioid use disorder (OUD) deep learning machine learning markerless tracking |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.953182/full |
| work_keys_str_mv | AT jacobrbumgarner machinelearninganddeeplearningframeworksfortheautomatedanalysisofpainandopioidwithdrawalbehaviors AT dariusdbeckerkrail machinelearninganddeeplearningframeworksfortheautomatedanalysisofpainandopioidwithdrawalbehaviors AT rhettcwhite machinelearninganddeeplearningframeworksfortheautomatedanalysisofpainandopioidwithdrawalbehaviors AT randyjnelson machinelearninganddeeplearningframeworksfortheautomatedanalysisofpainandopioidwithdrawalbehaviors |