Work

A pilot study on automated multi-task deep learning framework for pregnancy diagnostic and gestational age estimation in goats based on ultrasonography

Year: 2026

Type: article

Abstract: 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.

Source: Journal of Animal Reproduciton and Biotechnology

DOI: 10.12750/jarb.41.2.75