Background: This pilot study aims to develop an automated, multi-task deep learning system as a proof of concept for both the classification of pregnancy status and gestational day prediction in Korean crossbred goats. Methods: Two 1-year-old goats were followed longitudinally until mid to late gestation. A complete data set containing 11,092 high-resolution image frames (6,647 pregnant and 4,445 non-pregnant) was created from the video sequences. The dataset was segmented at the frame level with 80% allocated to the training partition (n = 8,875), 10% to the validation partition (n = 1,110), and 10% to the test partition (n = 1,107). Multi-task Four convolutional neural networks (ResNet18, EfficientNet-B0, MobileNetV3-Large, and ConvNeXt-Tiny) were trained following a multi-task learning strategy with the AdamW optimizer, OneCycle scheduling, and a hybrid Binary Cross- Entropy and Huber loss function. Results: Accuracy was achieved for the four architectures, with more than 99.5%. The MobileNetV3-Large model performed the best in terms of frame-level classification accuracy, with 100% sensitivity and specificity. For the regression-based task for predicting gestational days, EfficientNet-B0 got the best performance with a framelevel Mean Absolute Error (MAE) of 1.3 days. The video-level MAE of 0.882 days and the competitive video MAE of 1.204 days were the outcomes for the aggregated video sequences using ConvNeXt-Tiny and EfficientNet-B0, respectively. Conclusions: The present pilot study lays the foundation for the methodological feasibility of small optimized deep learning models for accurate monitoring of fetal development and for the automation of reproductive diagnostics.

ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
    Animals
    The use of experimental animals and data acquisitionby ultrasonography
    Image preprocessing and cropping
    Dataset splitting
    Deep learning infrastructure and model architecture
RESULTS
    Comparison of model performance related topregnancy diagnosis
    Gestational day prediction accuracy
    Day by day regression error analysis
DISCUSSION
CONCLUSION
REFERENCES

• Seungjun Lee(College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul 08826, Korea)
• Muhammad Bilal(College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul 08826, Korea, Farm Animal Clinical Training and Research Center, Institutes of Green-Bio Science and Technology, Seoul National University, Pyeongchang 25354, Korea)
• Jongki Cho(College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul 08826, Korea, Farm Animal Clinical Training and Research Center, Institutes of Green-Bio Science and Technology, Seoul National University, Pyeongchang 25354, Korea) Corresponding author
• Jaekyun Park(College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul 08826, Korea, College of Veterinary Medicine, Jeju National University, Jeju 63243, Korea)
• Mingyu Kim(Mari Animal Medical Laboratory, Yongin 17178, Korea)
• Hee-Woon Lee(Mari Animal Medical Laboratory, Yongin 17178, Korea)
• Yongjoo An(Department of Animal Health, Shin Ansan University, Ansan 15435, Korea)