A Deep Learning-Based System for Automatic Detection of Emesis With High Accuracy in Suncus murinus

Authors

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

Language

English

Publication Date

2-10-2025

Journal

Communications Biology

DOI

10.1038/s42003-025-07479-0

PMID

39930110

PMCID

PMC11811283

PubMedCentral® Posted Date

2-10-2025

PubMedCentral® Full Text Version

Post-print

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.

Keywords

Deep Learning, Vomiting, Animals, Shrews, Neural Networks, Computer, Gastrointestinal system, Machine learning

Published Open-Access

yes

Recommended Citation

Lu, Zengbing; Qiao, Yimeng; Huang, Xiaofei; et al., "A Deep Learning-Based System for Automatic Detection of Emesis With High Accuracy in Suncus murinus" (2025). Children’s Nutrition Research Center Staff Publications. 285.
https://digitalcommons.library.tmc.edu/staff_pub/285