Reproducible and fully automated testing of nocifensive behavior in mice

Dedek, Christopher; Azadgoleh, Mehdi A.; Prescott, Steven A.

doi:10.1016/j.crmeth.2023.100650

Cited by 7 publications

(4 citation statements)

References 76 publications

(125 reference statements)

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“…It involves training a computer model on a dataset, allowing it to make predictions or decisions independently. Automated Pain Recognition research has focused on discerning pain and pain intensity within clinical settings ( 87 ) and assessing responses to quantitative sensory testing in preclinical research ( 88 , 89 ). The following paragraphs will briefly outline and summarize the steps involved in APR.…”

Section: Automated Pain Recognitionmentioning

confidence: 99%

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From facial expressions to algorithms: a narrative review of animal pain recognition technologies

Chiavaccini,

Gupta,

Chiavaccini

2024

Front. Vet. Sci.

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Facial expressions are essential for communication and emotional expression across species. Despite the improvements brought by tools like the Horse Grimace Scale (HGS) in pain recognition in horses, their reliance on human identification of characteristic traits presents drawbacks such as subjectivity, training requirements, costs, and potential bias. Despite these challenges, the development of facial expression pain scales for animals has been making strides. To address these limitations, Automated Pain Recognition (APR) powered by Artificial Intelligence (AI) offers a promising advancement. Notably, computer vision and machine learning have revolutionized our approach to identifying and addressing pain in non-verbal patients, including animals, with profound implications for both veterinary medicine and animal welfare. By leveraging the capabilities of AI algorithms, we can construct sophisticated models capable of analyzing diverse data inputs, encompassing not only facial expressions but also body language, vocalizations, and physiological signals, to provide precise and objective evaluations of an animal's pain levels. While the advancement of APR holds great promise for improving animal welfare by enabling better pain management, it also brings forth the need to overcome data limitations, ensure ethical practices, and develop robust ground truth measures. This narrative review aimed to provide a comprehensive overview, tracing the journey from the initial application of facial expression recognition for the development of pain scales in animals to the recent application, evolution, and limitations of APR, thereby contributing to understanding this rapidly evolving field.

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Section: Automated Pain Recognitionmentioning

confidence: 99%

“…Different animal species pose unique challenges. Laboratory animals are usually confined to a limited environment, allowing more control over data acquisition and video recording quality ( 89 , 94 , 95 ). Horses can be manually restrained or confined in a stall ( 96 ).…”

Section: Automated Pain Recognitionmentioning

confidence: 99%

From facial expressions to algorithms: a narrative review of animal pain recognition technologies

Chiavaccini,

Gupta,

Chiavaccini

2024

Front. Vet. Sci.

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“…To address these challenges Dedek et al. 3 present an approach called RAMalgo that automates each stage of nociceptive testing—detection of the affected paw, application of the stimulus, and quantification of withdrawal latency—thereby offering the potential to improve standardization and objectivity of nociceptive testing. RAMalgo utilizes a motorized stage positioned below the test platform and is equipped to deliver mechanical, thermal (via infrared [IR] beam), or optogenetic (via blue light-emitting diode [LED]) stimulation at precise stimulus intensities across subjects and trials.…”

Section: Main Textmentioning

confidence: 99%

“… 4 To this end, Dedek et al. 3 establish the proof of principle that RAMalgo’s substage video can be leveraged not just for paw detection but also for quantifying spontaneous pain behaviors. Using pose estimation and subsequent unsupervised behavioral classification using machine learning, the authors extracted and quantified behavioral motifs associated with spontaneous non-evoked pain from substage video.…”

Section: Main Textmentioning

confidence: 99%

An automaton for preclinical pain testing

Graham,

Creed

2023

Cell Reports Methods

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Discordance between preclinical and clinical testing of NaV1.7-selective inhibitors for pain

Yang,

Xie,

Smith

et al. 2024

Pain

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The voltage-gated sodium channel NaV1.7 plays an important role in pain processing according to genetic data. Those data made NaV1.7 a popular drug target, especially since its relatively selective expression in nociceptors promised pain relief without the adverse effects associated with broader sodium channel blockade. Despite encouraging preclinical data in rodents, NaV1.7-selective inhibitors have not yet proven effective in clinical trials. Discrepancies between preclinical and clinical results should raise alarms. We reviewed preclinical and clinical reports on the analgesic efficacy of NaV1.7-selective inhibitors and found critical differences in several factors. Putting aside species differences, most preclinical studies tested young male rodents with limited genetic variability, inconsistent with the clinical population. Inflammatory pain was the most common preclinical chronic pain model whereas nearly all clinical trials focused on neuropathic pain despite some evidence suggesting NaV1.7 channels are not essential for neuropathic pain. Preclinical studies almost exclusively measured evoked pain whereas most clinical trials assessed average pain intensity without distinguishing between evoked and spontaneous pain. Nearly all preclinical studies gave a single dose of drug unlike the repeat dosing used clinically, thus precluding preclinical data from demonstrating whether tolerance or other slow processes occur. In summary, preclinical testing of NaV1.7-selective inhibitors aligned poorly with clinical testing. Beyond issues that have already garnered widespread attention in the pain literature, our results highlight the treatment regimen and choice of pain model as areas for improvement.

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Reproducible and fully automated testing of nocifensive behavior in mice

Cited by 7 publications

References 76 publications

From facial expressions to algorithms: a narrative review of animal pain recognition technologies

From facial expressions to algorithms: a narrative review of animal pain recognition technologies

An automaton for preclinical pain testing

Discordance between preclinical and clinical testing of NaV1.7-selective inhibitors for pain

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