A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus
Abstract Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or reviewing recorded behaviour, which are laborious, time-consuming processes that are susceptible to operator error. With rapid advancements in deep learning, automated animal behaviour quanti...
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Nature Portfolio
2025-02-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-07479-0 |
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| author | Zengbing Lu Yimeng Qiao Xiaofei Huang Dexuan Cui Julia Y. H. Liu Man Piu Ngan Luping Liu Zhixin Huang Zi-Tong Li Lingqing Yang Aleena Khalid Yingyi Deng Sze Wa Chan Longlong Tu John A. Rudd |
| author_facet | Zengbing Lu Yimeng Qiao Xiaofei Huang Dexuan Cui Julia Y. H. Liu Man Piu Ngan Luping Liu Zhixin Huang Zi-Tong Li Lingqing Yang Aleena Khalid Yingyi Deng Sze Wa Chan Longlong Tu John A. Rudd |
| author_sort | Zengbing Lu |
| collection | DOAJ |
| description | Abstract Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or reviewing recorded behaviour, which are laborious, time-consuming processes that are susceptible to operator error. With rapid advancements in deep learning, automated animal behaviour quantification tools with high accuracy have emerged. In this study, we pioneere the use of both three-dimensional convolutional neural networks and self-attention mechanisms to develop the Automatic Emesis Detection (AED) tool for the quantification of emesis in S. murinus, achieving an overall accuracy of 98.92%. Specifically, we use motion-induced emesis videos as training datasets, with validation results demonstrating an accuracy of 99.42% for motion-induced emesis. In our model generalisation and application studies, we assess the AED tool using various emetics, including resiniferatoxin, nicotine, copper sulphate, naloxone, U46619, cyclophosphamide, exendin-4, and cisplatin. The prediction accuracies for these emetics are 97.10%, 100%, 100%, 97.10%, 98.97%, 96.93%, 98.91%, and 98.41%, respectively. In conclusion, employing deep learning-based automatic analysis improves efficiency and accuracy and mitigates human bias and errors. Our study provides valuable insights into the development of deep learning neural network models aimed at automating the analysis of various behaviours in S. murinus, with potential applications in preclinical research and drug development. |
| format | Article |
| id | doaj-art-8fe741e379f246a49083c522cd55b940 |
| institution | DOAJ |
| issn | 2399-3642 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-8fe741e379f246a49083c522cd55b9402025-08-20T02:48:27ZengNature PortfolioCommunications Biology2399-36422025-02-018111110.1038/s42003-025-07479-0A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinusZengbing Lu0Yimeng Qiao1Xiaofei Huang2Dexuan Cui3Julia Y. H. Liu4Man Piu Ngan5Luping Liu6Zhixin Huang7Zi-Tong Li8Lingqing Yang9Aleena Khalid10Yingyi Deng11Sze Wa Chan12Longlong Tu13John A. Rudd14Emesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongDepartment of Chemical and Biological Engineering, The Hong Kong University of Science and TechnologyEmesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongEmesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongEmesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongEmesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongEmesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongSchool of Health Sciences, University of ManchesterEmesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongEmesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongEmesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongEmesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongSchool of Health Sciences, Saint Francis UniversityDepartment of Pediatrics, USDA/ARS Children’s Nutrition Research Center, Baylor College of MedicineEmesis Research Group, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong KongAbstract Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or reviewing recorded behaviour, which are laborious, time-consuming processes that are susceptible to operator error. With rapid advancements in deep learning, automated animal behaviour quantification tools with high accuracy have emerged. In this study, we pioneere the use of both three-dimensional convolutional neural networks and self-attention mechanisms to develop the Automatic Emesis Detection (AED) tool for the quantification of emesis in S. murinus, achieving an overall accuracy of 98.92%. Specifically, we use motion-induced emesis videos as training datasets, with validation results demonstrating an accuracy of 99.42% for motion-induced emesis. In our model generalisation and application studies, we assess the AED tool using various emetics, including resiniferatoxin, nicotine, copper sulphate, naloxone, U46619, cyclophosphamide, exendin-4, and cisplatin. The prediction accuracies for these emetics are 97.10%, 100%, 100%, 97.10%, 98.97%, 96.93%, 98.91%, and 98.41%, respectively. In conclusion, employing deep learning-based automatic analysis improves efficiency and accuracy and mitigates human bias and errors. Our study provides valuable insights into the development of deep learning neural network models aimed at automating the analysis of various behaviours in S. murinus, with potential applications in preclinical research and drug development.https://doi.org/10.1038/s42003-025-07479-0 |
| spellingShingle | Zengbing Lu Yimeng Qiao Xiaofei Huang Dexuan Cui Julia Y. H. Liu Man Piu Ngan Luping Liu Zhixin Huang Zi-Tong Li Lingqing Yang Aleena Khalid Yingyi Deng Sze Wa Chan Longlong Tu John A. Rudd A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus Communications Biology |
| title | A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus |
| title_full | A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus |
| title_fullStr | A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus |
| title_full_unstemmed | A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus |
| title_short | A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus |
| title_sort | deep learning based system for automatic detection of emesis with high accuracy in suncus murinus |
| url | https://doi.org/10.1038/s42003-025-07479-0 |
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