Evaluation of Machine Learning Models for TT-POCUS Based Prognostic Prediction of Did-Not-Finish and First-Treatment Failure Cattle Diagnosed with Bovine Respiratory Disease

Bovine respiratory disease remains the leading cause of morbidity and mortality in feedyard cattle and is associated with substantial economic and animal welfare consequences. Prognostic tools remain limited, but combining objective diagnostic measures with predictive modeling may improve clinical decision-making. This study evaluated the performance of machine learning models incorporating targeted thoracic point-of-care ultrasound (TT-POCUS)-derived variables to predict did-not-finish (DNF) and first-treatment failure (FTF) outcomes in feedyard cattle diagnosed with bovine respiratory disease (BRD). First-treatment cattle were evaluated for both FTF and DNF outcomes, whereas chronic cattle were evaluated for DNF outcomes following enrollment. A prospective cohort observational study was conducted in cattle receiving either first treatment or chronic treatment for BRD. TT-POCUS examinations were performed at enrollment to assess pulmonary abnormalities, including lung consolidation, pleural irregularities, B-lines, and lesion scores. A total of 1422 first-treatment cattle and 150 chronic cattle were enrolled. Six supervised machine learning algorithms were trained using TT-POCUS variables and demographic data. For FTF prediction, logistic regression demonstrated the highest AUC (0.71). For first-treatment DNF, logistic regression again achieved the highest AUC (0.80). In chronic cattle, the perceptron model demonstrated the highest AUC (0.84). Overall, TT-POCUS-derived variables provided meaningful prognostic information and may support improved treatment decisions, antimicrobial stewardship, and animal welfare in feedyard cattle.