ReviewFrontiers in neuroscience2022
Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors.
Review in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- A widespread internal brain state for fentanyl withdrawal.bioRxiv : the preprint server for biology · 2026Article
- Anterior Cruciate Ligament Tissue Engineering: Biological Principles, Engineered Substitutes, and Preclinical Outcomes.Bioengineering (Basel, Switzerland) · 2026Review
- Cannabinoid efficacy for pain: dose, chronicity and route.Frontiers in neuroscience · 2026Review
- Integrating AI, Machine Learning, and Animal Models for Precision Oncology: Bridging Preclinical and Clinical Gaps.ACS pharmacology & translational science · 2025Article
- A machine learning tool with light-based image analysis for automatic classification of 3D pain behaviors.Cell reports methods · 2025Article
- Exploring potential neuroimaging biomarkers for the response to non-steroidal anti-inflammatory drugs in episodic migraine.The journal of headache and pain · 2024Article
- The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives.Healthcare (Basel, Switzerland) · 2024Review
- Two-year Opioid Prescription Trends in Local Sanitary Agency Naples 3 South, Campania Region, Italy. Descriptive Analyses and AI-based Translational Perspectives.Translational medicine @ UniSa · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.