Deep learning for video-based automated pain recognition in rabbits

Marcelo Feighelstein; Yamit Ehrlich; Li Naftaly; Miriam Alpin; Shenhav Nadir; Ilan Shimshoni; Renata H. Pinho; Stelio P.L. Luna; Anna Zamansky

doi:10.1038/s41598-023-41774-2

Deep learning for video-based automated pain recognition in rabbits

Marcelo Feighelstein, Yamit Ehrlich, Li Naftaly, Miriam Alpin, Shenhav Nadir, Ilan Shimshoni, Renata H. Pinho, Stelio P.L. Luna, Anna Zamansky

Department of Information Systems

Research output: Contribution to journal › Article › peer-review

Abstract

Despite the wide range of uses of rabbits (Oryctolagus cuniculus) as experimental models for pain, as well as their increasing popularity as pets, pain assessment in rabbits is understudied. This study is the first to address automated detection of acute postoperative pain in rabbits. Using a dataset of video footage of n = 28 rabbits before (no pain) and after surgery (pain), we present an AI model for pain recognition using both the facial area and the body posture and reaching accuracy of above 87%. We apply a combination of 1 sec interval sampling with the Grayscale Short-Term stacking (GrayST) to incorporate temporal information for video classification at frame level and a frame selection technique to better exploit the availability of video data.

Original language	English
Article number	14679
Journal	Scientific Reports
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41598-023-41774-2
State	Published - 6 Sep 2023

Bibliographical note

Funding Information:
The research was partially supported by the Israel Ministry Agriculture and Rural Development. The first author was additionally supported by the Data Science Research Center (DSRC), University of Haifa. The authors would like to thank Hovav Gazit for his support and guidance in mentoring the students of the Computer Graphics and Multimedia Laboratory, The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering at the Technion. A special thank you to Yaron Yossef for his constant help and support.

Funding Information:
The research was partially supported by the Israel Ministry Agriculture and Rural Development. The first author was additionally supported by the Data Science Research Center (DSRC), University of Haifa. The authors would like to thank Hovav Gazit for his support and guidance in mentoring the students of the Computer Graphics and Multimedia Laboratory, The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering at the Technion. A special thank you to Yaron Yossef for his constant help and support.

Publisher Copyright:
© 2023, Springer Nature Limited.

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10.1038/s41598-023-41774-2

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title = "Deep learning for video-based automated pain recognition in rabbits",

abstract = "Despite the wide range of uses of rabbits (Oryctolagus cuniculus) as experimental models for pain, as well as their increasing popularity as pets, pain assessment in rabbits is understudied. This study is the first to address automated detection of acute postoperative pain in rabbits. Using a dataset of video footage of n = 28 rabbits before (no pain) and after surgery (pain), we present an AI model for pain recognition using both the facial area and the body posture and reaching accuracy of above 87%. We apply a combination of 1 sec interval sampling with the Grayscale Short-Term stacking (GrayST) to incorporate temporal information for video classification at frame level and a frame selection technique to better exploit the availability of video data.",

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Deep learning for video-based automated pain recognition in rabbits

Abstract

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