Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs

SIMPLE SUMMARY: Scoring lesions in slaughtered pigs can provide useful feedback to the swine industry, although the systematic recording of lesions is very challenging and time consuming. Artificial intelligence offers interesting opportunities to solve highly repetitive tasks, such as those performed by veterinarians at postmortem inspection in high-throughput slaughterhouses and to consistently analyze large amounts of data. The present investigation indicates that enzootic pneumonia-like lesions can be effectively detected and quantified through artificial intelligence methods under routine slaughter conditions. ABSTRACT: The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time-consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial intelligence(AI) has gained traction in many fields of research, including livestock production. In particular, AI-based methods appear able to solve highly repetitive tasks and to consistently analyze large amounts of data, such as those collected by veterinarians during postmortem inspection in high-throughput slaughterhouses. The present study aims to develop an AI-based method capable of recognizing and quantifying enzootic pneumonia-like lesions on digital images captured from slaughtered pigs under routine abattoir conditions. Overall, the data indicate that the AI-based method proposed herein could properly identify and score enzootic pneumonia-like lesions without interfering with the slaughter chain routine. According to European legislation, the application of such a method avoids the handling of carcasses and organs, decreasing the risk of microbial contamination, and could provide further alternatives in the field of food hygiene.