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Home / Equine Research Bank / PLoS One / Sharing pain: Using pain domain transfer for video recogniti...
PloS one2022; 17(3); e0263854; doi: 10.1371/journal.pone.0263854

Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses.

Abstract: Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition.
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Publication Date: 2022-03-04 PubMed ID: 35245288PubMed Central: PMC8896717DOI: 10.1371/journal.pone.0263854Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This study explores the use of a computer model trained to recognize acute pain in horses from video data as a tool for detecting subtler, chronic pain associated with orthopedic conditions. Via domain transfer methods and baseline expertise, the researchers demonstrate the potential of this technology as an early detection tool for equine orthopedic disorders.

Clarification of Research Purpose

  • This research aims to create a machine learning model that can identify subtle signs of long-term orthopedic pain in horses.
  • The need for this arises from the difficulty of identifying such pain early on, often leading to late-stage diagnosis and substantial consequences such as euthanasia.
  • Currently, consistently accurate labels for training such a model are difficult to establish since it’s challenging even for human experts to distinguish these subtle pain indicators.

Data and Model Training

  • A dataset of video footage exhibiting horses with acute, experimental pain was used to train the model. This was done since acute pain manifestations are less ambiguous and are easier to label than subtle, chronic pain.
  • The hope was that a model trained in this manner could then identify more nuanced and varied signs of long-term orthopedic pain in horses by transferring knowledge from the acute pain domain.

Application & Study Findings

  • The study presents an empirical investigation into various domain transfer methods, that is, how to best train the model on acute pain data to recognize chronic orthopedic pain.
  • The effectiveness of the recognition model, trained on clear experimental pain, is then tested on the orthopedic dataset.
  • The results revealed the potential of the model in identifying orthopedic pain, implicitly demonstrating the practical use of this technology for early diagnosis.

Significance and Future Remarks

  • The findings open up a discussion on the challenges inherent to using machine learning on real-world animal behavior datasets and how best to address these.
  • With this study, best practices can be established for similar fine-grained action recognition tasks in the future.
  • Overall, this research has important implications for veterinary medicine and for furthering humane treatment of animals, by aiding in the early detection and treatment of painful medical conditions.
  • The code used in this research is publicly available, fostering further innovation and application by other researchers in this field.

Cite This Article

APA
Broomé S, Ask K, Rashid-Engström M, Haubro Andersen P, Kjellström H. (2022). Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses. PLoS One, 17(3), e0263854. https://doi.org/10.1371/journal.pone.0263854

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 17
Issue: 3
Pages: e0263854

Researcher Affiliations

Broomé, Sofia
  • Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden.
Ask, Katrina
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Rashid-Engström, Maheen
  • Department of Computer Science, University of California, Davis, California, United States of America.
  • Univrses, Stockholm, Sweden.
Haubro Andersen, Pia
  • Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Kjellström, Hedvig
  • Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Silo AI, Stockholm, Sweden.

MeSH Terms

  • Animals
  • Communications Media
  • Horses
  • Pain / veterinary

Conflict of Interest Statement

The authors have declared that no competing interests exist.

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