Epidemiological Factors Associated with Gross Diagnosis of Pulmonary Pathology in Feedyard Mortalities

Respiratory disease continues to be the major cause of mortality in feedyard cattle, with bronchopneumonia (BP) and acute interstitial pneumonia (AIP) as the two most common syndromes. Recent studies described a combination of these pathological lesions with the presence of AIP in the caudodorsal lungs and BP in the cranioventral lungs of necropsied cattle. This pulmonary pathology has been described as bronchopneumonia with an interstitial pneumonia (BIP). The epidemiological characteristics of BIP in U.S. feedyard cattle are yet to be described. This study’s objectives were to describe the agreement between feedyard clinical and necropsy gross diagnosis and to characterize epidemiological factors associated with four gross pulmonary diagnoses (AIP, BIP, BP, and Normal pulmonary tissue) observed during feedyard cattle necropsies. Systemic necropsies were performed at six feedyards in U.S. high plains region, and gross pulmonary diagnoses were established. Historical data were added to the dataset, including sex, days on feed at death (DOFDEATH), arrival weight, treatment count, and feedyard diagnosis. Generalized linear models were used to evaluate epidemiological factors associated with the probability of each pulmonary pathology. Comparing feedyard clinical diagnosis with gross pathological diagnosis revealed relatively low agreement and the frequency of agreement varied by diagnosis. The likelihood of AIP at necropsy was higher for heifers than steers and in the 100–150 DOFDEATH category compared with the 0–50 DOFDEATH (p = 0.05). The likelihood of BIP increased after the first treatment, whereas the DOFDEATH 0–50 category had a lower likelihood compared with the 150–200 category (p = 0.05). These findings highlight the importance of necropsy for final diagnosis and can aid the development of future diagnosis and therapeutic protocols for pulmonary diseases

Image Classification and Automated Machine Learning to Classify Lung Pathologies in Deceased Feedlot Cattle

Bovine respiratory disease (BRD) and acute interstitial pneumonia (AIP) are the main reported respiratory syndromes (RSs) causing significant morbidity and mortality in feedlot cattle. Recently, bronchopneumonia with an interstitial pattern (BIP) was described as a concerning emerging feedlot lung disease. Necropsies are imperative to assist lung disease diagnosis and pinpoint feedlot management sectors that require improvement. However, necropsies can be logistically challenging due to location and veterinarians’ time constraints. Technology advances allow image collection for veterinarians’ asynchronous evaluation, thereby reducing challenges. This study’s goal was to develop image classification models using machine learning to determine RS diagnostic accuracy in right lateral necropsied feedlot cattle lungs. Unaltered and cropped lung images were labeled using gross and histopathology diagnoses generating four datasets: unaltered lung images labeled with gross diagnoses, unaltered lung images labeled with histopathological diagnoses, cropped images labeled with gross diagnoses, and cropped images labeled with histopathological diagnoses. Datasets were exported to create image classification models, and a best trial was selected for each model based on accuracy. Gross diagnoses accuracies ranged from 39 to 41% for unaltered and cropped images. Labeling images with histopathology diagnoses did not improve average accuracies; 34–38% for unaltered and cropped images. Moderately high sensitivities were attained for BIP (60–100%) and BRD (20–69%) compared to AIP (0–23%). The models developed still require fine-tuning; however, they are the first step towards assisting veterinarians’ lung diseases diagnostics in field necropsies