AI-based prediction and detection of early-onset of digital dermatitis in dairy cows using infrared thermography.

Digital dermatitis (DD) is a common foot disease that can cause lameness, decreased milk production and fertility decline in cows. The prediction and early detection of DD can positively impact animal welfare and profitability of the dairy industry. This study applies deep learning-based computer vision techniques for early onset detection and prediction of DD using infrared thermography (IRT) data.

Machine learning approaches to predict and detect early-onset of digital dermatitis in dairy cows using sensor data.

The present study aimed to employ machine learning algorithms based on sensor behavior data for (1) early-onset detection of digital dermatitis (DD) and (2) DD prediction in dairy cows. Our machine learning model, which was based on the Tree-Based Pipeline Optimization Tool (TPOT) automatic machine learning method, for DD detection on day 0 of the appearance of the clinical signs has reached an accuracy of 79% on the test set, while the model for the prediction of DD 2 days prior to the appearance of the first clinical signs, which was a combination of K-means and TPOT, has reached an accuracy of 64%. The proposed machine learning models have the potential to help achieve a real-time automated tool for monitoring and diagnosing DD in lactating dairy cows based on sensor data in conventional dairy barn environments.

Differential Strain-dependent Ovarian and Metabolic Responses in a Mouse Model of PCOS.

Several mouse models have been developed to study polycystic ovarian syndrome (PCOS), a leading cause of infertility in women. Treatment of mice with DHT for 90 days causes ovarian and metabolic phenotypes similar to women with PCOS. We used this 90-day DHT treatment paradigm to investigate the variable incidence and heterogeneity in 2 inbred mouse strains, NOD/ShiLtJ and 129S1/SvlmJ.