ArticleAnimals : an open access journal from MDPI2021
Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs.
Article in Animals : an open access journal from MDPI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- Computer vision system for assessing pig welfare indicators on carcasses.Veterinary and animal science · 2026Article
- An artificial intelligence-based diagnosis system for the identification of helminth parasitic infections in mithun and allied bovines.Scientific reports · 2025Article
- Non-Invasive Human-Free Diagnosis Methods for Assessing Pig Welfare at Abattoirs: A Systematic Review.Animals : an open access journal from MDPI · 2025Review
- Detecting ear lesions in slaughtered pigs through open-source convolutional neural networks.Porcine health management · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Scoring of swine lung images: a comparison between a computer vision system and human evaluators.Veterinary research · 2025Article
- Cross-species AI: shifting a convolutional neural network from pigs to lambs to detect pneumonia at slaughter.Frontiers in veterinary science · 2025Article
- Scoring Enzootic Pneumonia-like Lesions in Slaughtered Pigs: Traditional vs. Artificial-Intelligence-Based Methods.Pathogens (Basel, Switzerland) · 2023Article
- Opportunities for Regulatory Authorities to Assess Animal-Based Measures at the Slaughterhouse Using Sensor Technology and Artificial Intelligence: A Review.Animals : an open access journal from MDPI · 2023Review
- Welfare of calves.EFSA journal. European Food Safety Authority · 2023Article
- Meat safety legislation and its opportunities and hurdles for innovative approaches: A review.Food control · 2022Review
- Welfare of pigs on farm.EFSA journal. European Food Safety Authority · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.