ArticlePathogens (Basel, Switzerland)2023
Scoring Enzootic Pneumonia-like Lesions in Slaughtered Pigs: Traditional vs. Artificial-Intelligence-Based Methods.
Article in Pathogens (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- 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
- Scoring of swine lung images: a comparison between a computer vision system and human evaluators.Veterinary research · 2025Article
- Impact of porcine respiratory disease complex on carcass weight and meatiness: quantitative insights from a mixed-model analysis.BMC veterinary research · 2024Article
- An Integrated Analysis of Abattoir Lung Lesion Scores and Antimicrobial Use in Italian Heavy Pig Finishing Farms.Animals : an open access journal from MDPI · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Artificial-intelligence-based methods are regularly used in the biomedical sciences, mainly in the field of diagnostic imaging. Recently, convolutional neural networks have been trained to score pleurisy and pneumonia in slaughtered pigs. The aim of this study is to further evaluate the performance of a convolutional neural network when compared with the gold standard (i.e., scores provided by a skilled operator along the slaughter chain through visual inspection and palpation). In total, 441 lungs (180 healthy and 261 diseased) are included in this study. Each lung was scored according to traditional methods, which represent the gold standard (Madec's and Christensen's grids). Moreover, the same lungs were photographed and thereafter scored by a trained convolutional neural network. Overall, the results reveal that the convolutional neural network is very specific (95.55%) and quite sensitive (85.05%), showing a rather high correlation when compared with the scores provided by a skilled veterinarian (Spearman's coefficient = 0.831,
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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.