Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings
Abstract Facial expressions are essential in animal communication, and facial expression-based pain scales have been developed for different species. Automated pain recognition offers a valid alternative to manual annotation with growing evidence across species. This study applied machine learning (...
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Nature Portfolio
2024-11-01
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| Online Access: | https://doi.org/10.1038/s41598-024-78494-0 |
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| author | Ludovica Chiavaccini Anjali Gupta Nicole Anclade Guido Chiavaccini Chiara De Gennaro Alanna N. Johnson Diego A. Portela Marta Romano Enzo Vettorato Daniela Luethy |
| author_facet | Ludovica Chiavaccini Anjali Gupta Nicole Anclade Guido Chiavaccini Chiara De Gennaro Alanna N. Johnson Diego A. Portela Marta Romano Enzo Vettorato Daniela Luethy |
| author_sort | Ludovica Chiavaccini |
| collection | DOAJ |
| description | Abstract Facial expressions are essential in animal communication, and facial expression-based pain scales have been developed for different species. Automated pain recognition offers a valid alternative to manual annotation with growing evidence across species. This study applied machine learning (ML) methods, using a pre-trained VGG-16 base and a Support Vector Machine classifier to automate pain recognition in caprine patients in hospital settings, evaluating different frame extraction rates and validation techniques. The study included goats of different breed, age, sex, and varying medical conditions presented to the University of Florida’s Large Animal Hospital. Painful status was determined using the UNESP-Botucatu Goat Acute Pain Scale. The final dataset comprised images from 40 goats (20 painful, 20 non-painful), with 2,253 ‘non-painful’ and 3,154 ‘painful’ images at 1 frame per second (FPS) extraction rate and 7,630 ‘non-painful’ and 9,071 ‘painful’ images at 3 FPS. Images were used to train deep learning-based models with different approaches. The model input was raw images, and pain presence was the target attribute (model output). For the single train-test split and 5-fold cross-validation, the models achieved approximately 80% accuracy, while the subject-wise 10-fold cross-validation showed mean accuracies above 60%. These findings suggest ML’s potential in goat pain assessment. |
| format | Article |
| id | doaj-art-8129353cfe2c492f8adea19dfd1bab3a |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-8129353cfe2c492f8adea19dfd1bab3a2025-08-20T02:50:07ZengNature PortfolioScientific Reports2045-23222024-11-0114111110.1038/s41598-024-78494-0Automated acute pain prediction in domestic goats using deep learning-based models on video-recordingsLudovica Chiavaccini0Anjali Gupta1Nicole Anclade2Guido ChiavacciniChiara De Gennaro3Alanna N. Johnson4Diego A. Portela5Marta Romano6Enzo Vettorato7Daniela Luethy8Department of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of FloridaDepartment of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of FloridaDepartment of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of FloridaDepartment of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of FloridaDepartment of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of FloridaDepartment of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of FloridaDepartment of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of FloridaDepartment of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of FloridaDepartment of Clinical Studies – New Bolton Center, School of Veterinary Medicine, University of PennsylvaniaAbstract Facial expressions are essential in animal communication, and facial expression-based pain scales have been developed for different species. Automated pain recognition offers a valid alternative to manual annotation with growing evidence across species. This study applied machine learning (ML) methods, using a pre-trained VGG-16 base and a Support Vector Machine classifier to automate pain recognition in caprine patients in hospital settings, evaluating different frame extraction rates and validation techniques. The study included goats of different breed, age, sex, and varying medical conditions presented to the University of Florida’s Large Animal Hospital. Painful status was determined using the UNESP-Botucatu Goat Acute Pain Scale. The final dataset comprised images from 40 goats (20 painful, 20 non-painful), with 2,253 ‘non-painful’ and 3,154 ‘painful’ images at 1 frame per second (FPS) extraction rate and 7,630 ‘non-painful’ and 9,071 ‘painful’ images at 3 FPS. Images were used to train deep learning-based models with different approaches. The model input was raw images, and pain presence was the target attribute (model output). For the single train-test split and 5-fold cross-validation, the models achieved approximately 80% accuracy, while the subject-wise 10-fold cross-validation showed mean accuracies above 60%. These findings suggest ML’s potential in goat pain assessment.https://doi.org/10.1038/s41598-024-78494-0Artificial intelligenceAcute painDeep learningFacial expressionGoatsPain measurement |
| spellingShingle | Ludovica Chiavaccini Anjali Gupta Nicole Anclade Guido Chiavaccini Chiara De Gennaro Alanna N. Johnson Diego A. Portela Marta Romano Enzo Vettorato Daniela Luethy Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings Scientific Reports Artificial intelligence Acute pain Deep learning Facial expression Goats Pain measurement |
| title | Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings |
| title_full | Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings |
| title_fullStr | Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings |
| title_full_unstemmed | Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings |
| title_short | Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings |
| title_sort | automated acute pain prediction in domestic goats using deep learning based models on video recordings |
| topic | Artificial intelligence Acute pain Deep learning Facial expression Goats Pain measurement |
| url | https://doi.org/10.1038/s41598-024-78494-0 |
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