Automated Classification for Visual-Only Postmortem Inspection of Porcine Pathology

McKenna, Stephen J.; Amaral, Teresa; Kyriazakis, Ι.

doi:10.1109/tase.2019.2960106

Cited by 10 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this respect, it should be remarked that over 18 000 articles are currently available in the US National Library of Medicine National Institutes of Health by typing "artificial intelligence" and "medicine" as keywords, covering most of the disciplines of human medicine, including pathology [28]. On the contrary, very few papers have yet been published regarding the application of AI to veterinary pathology [29][30][31][32]. Interestingly, Sanchez-Vazquez et al [31] applied a machine learning methodology to identify associations among different disease conditions in slaughtered pigs, the scoring carried out by swine veterinarians acting as their data source.…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, Sanchez-Vazquez et al [31] applied a machine learning methodology to identify associations among different disease conditions in slaughtered pigs, the scoring carried out by swine veterinarians acting as their data source. Very recently, McKenna et al [32] applied machine learning to detect pericarditis and hepatis parasitic lesions at post-mortem inspection in pigs.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Scoring pleurisy in slaughtered pigs using convolutional neural networks

Trachtman

Bergamini

Palazzi

et al. 2020

Vet Res

View full text Add to dashboard Cite

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. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article'

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Scoring pleurisy in slaughtered pigs using convolutional neural networks

Trachtman

Bergamini

Palazzi

et al. 2020

Vet Res

View full text Add to dashboard Cite

show abstract

“…Pluck lesions are lesions of a group of organs that belong to pigs’ red offal [ 50 ]. This section focuses on lesions of the lung and the pleura, which are considered of great importance, while lesions concerning the other organs of the pluck, such as the heart (e.g., pericarditis) and liver (e.g., ascaridiosis), are not included due to the lack of specific scoring methods.…”

Section: Pluck Lesionsmentioning

confidence: 99%

“…In addition to this, a certain rate of discrepancy in the assessment of lesions between observers may not provide reliable information, as previously reported [ 60 ]. There is a growing interest in overcoming such obstacles by using automated inspection systems [ 50 ]. These methods use convolutional neural networks (CNNs) to designate pathological conditions probabilities to image locations.…”

Section: Future Perspectivesmentioning

confidence: 99%

“…These methods use convolutional neural networks (CNNs) to designate pathological conditions probabilities to image locations. Such images of probabilities can be converted in a heat map, which is submitted to a further classification, based on statistical methods, that decide whether to assign a certain pathology to an organ or not [ 50 ]. CNNs-based systems can be trained in order to detect and analyze a specific part of the animal body [ 100 ].…”

Section: Future Perspectivesmentioning

confidence: 99%

See 1 more Smart Citation

Abattoir-Based Measures to Assess Swine Welfare: Analysis of the Methods Adopted in European Slaughterhouses

Luca

Zanardi

Alborali

et al. 2021

Animals

View full text Add to dashboard Cite

The assessment of swine welfare requires feasible, reliable, and reasonable indicators. On-farm evaluation of pig welfare can provide valuable information to veterinarians and farmers. However, such protocols can result expensive and time-consuming. With this regard, an interest in the appraisal of swine welfare at abattoir has grown over the recent years. In particular, the use of certain lesions collected directly from slaughtered animals to determine the welfare status of pigs has been evaluated by several authors. In the present review, the different methods developed to score lesions collected directly from the body and the viscera of animals slaughtered in European abattoirs (“abattoir-based measures”) are presented. The text specifically focuses on the methods currently available in the literature for the scoring of body, pluck and gastric lesions during post-mortem activities. Moreover, the strengths and weaknesses of abattoir-based measures schemes are discussed. To conclude, the future perspectives of the assessment of pig welfare at the slaughterhouse are described, appealing for a benchmarking system that can be systematically used by veterinarians and other professional figures involved in the process.

show abstract