Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs.

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.

Scoring pleurisy in slaughtered pigs using convolutional neural networks.

Diseases of the respiratory system are known to negatively impact the profitability of the pig industry, worldwide. Considering the relatively short lifespan of pigs, lesions can be still evident at slaughter, where they can be usefully recorded and scored. Therefore, the slaughterhouse represents a key check-point to assess the health status of pigs, providing unique and valuable feedback to the farm, as well as an important source of data for epidemiological studies. Although relevant, scoring lesions in slaughtered pigs represents a very time-consuming and costly activity, thus making difficult their systematic recording. The present study has been carried out to train a convolutional neural network-based system to automatically score pleurisy in slaughtered pigs. The automation of such a process would be extremely helpful to enable a systematic examination of all slaughtered livestock. Overall, our data indicate that the proposed system is well able to differentiate half carcasses affected with pleurisy from healthy ones, with an overall accuracy of 85.5%. The system was better able to recognize severely affected half carcasses as compared with those showing less severe lesions. The training of convolutional neural networks to identify and score pneumonia, on the one hand, and the achievement of trials in large capacity slaughterhouses, on the other, represent the natural pursuance of the present study. As a result, convolutional neural network-based technologies could provide a fast and cheap tool to systematically record lesions in slaughtered pigs, thus supplying an enormous amount of useful data to all stakeholders in the pig industry.

"SILAB for Africa": An Innovative Information System Supporting the Veterinary African Laboratories.

Introduction: To support African veterinary laboratory services, the Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise puts in place an operational system called "SILAB for Africa" (SILABFA); this is a web application used by a laboratory information management system to support laboratory diagnostic activities and to meet the needs of various African countries. SILABFA was designed to collect and manage all necessary information on samples, tests, and test results.Methods: The system involves the entry of sample data on arrival, the tracking of samples through the various sections of the laboratory, and the collection of test results. It automates the generation of test reports and monitors outbreaks through data interrogation functions and eliminates multiple registrations of the same data on paper records. SILABFA is currently installed in Namibia, Botswana, Zambia, Zimbabwe, Tanzania, Uganda, Kenya, Ethiopia, and Cameroon, and installation in Senegal and Ivory Coast is planned for the next few months. After some years of SILABFA usage, it was natural to want to utilize more and more data collected in a homogeneous and consistent way for epidemiological purposes and to cover informative debts toward ministries and other organizations.Conclusion: To improve the availability of good, detailed, and reliable data, as the epidemiological information, SILABFA has been linked to the local animal identification, registration, and traceability system and other relevant national information systems.