A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus

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.