Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 19, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Identifying associations between pig pathologies using a multi-dimensional machine learning methodology.

Manuel J Sanchez-Vazquez1, Mirjam Nielen, Sandra A Edwards

  • 1Scottish Agricultural College, Kings Buildings, West Mains Road, Edinburgh EH9 3JG, UK.

BMC Veterinary Research
|September 4, 2012
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Preparing for exotic foot-and-mouth disease virus outbreaks in the European neighbourhood: cross-neutralisation of internationally available vaccines against South American serotype O and serotype A field isolates.

Vaccine·2026
Same author

Randomised Badger Culling Trial-no effects of widespread badger culling on tuberculosis in cattle: comment on Mills, Woodroffe and Donnelly (2024a, 2024b).

Royal Society open science·2025
Same author

Equine veterinarians' care priorities regarding vaccination, colic, lameness and pre-purchase scenarios.

Equine veterinary journal·2025
Same author

Network meta-analysis based ranking of dry off interventions to cure or prevent intramammary infections in dairy cows.

Preventive veterinary medicine·2025
Same author

Survey-based investigation of sports and leisure horse owners' approaches to, and expectations of, equine veterinary care.

The Veterinary record·2024
Same author

The DECIDE project: from monitoring and surveillance data to decision-support for farmers and veterinarians.

Open research Europe·2024
Same journal

Echocardiographic effects of medetomidine, butorphanol, and their combination in donkeys.

BMC veterinary research·2026
Same journal

Duplex MIRA assays for simultaneous detection of novel goose astrovirus and goose circovirus.

BMC veterinary research·2026
Same journal

Correction: Anesthetic and analgesic management of Svalbard rock ptarmigan (Lagopus muta hyperborea) undergoing experimental stereotaxic neurosurgery.

BMC veterinary research·2026
Same journal

Optimal dietary nano-VD₃ enhances growth performance and bone development in broilers through remodeling the gut microbiome and metabolites.

BMC veterinary research·2026
Same journal

Dietary anti- Escherichia coli K88 IgY enhances immune response and intestinal barrier function in piglets challenged with Escherichia coli K88.

BMC veterinary research·2026
Same journal

Correction: Exploration of the effect and mechanism of indirubin on infectious bovine rhinotracheitis virus based on network pharmacology.

BMC veterinary research·2026
See all related articles

Machine learning identified links between pig pathologies, revealing connections like septicaemia with bacterial infections and hepatic scarring with parasitic and bacterial issues. This aids understanding and control strategies for multiple swine diseases.

Area of Science:

  • Veterinary Pathology
  • Machine Learning Applications
  • Swine Health Management

Background:

  • Abattoir findings are critical for pig production and food safety.
  • Co-existing pathologies in pig herds often have unknown interrelationships.
  • Understanding pathology associations can improve disease control strategies.

Purpose of the Study:

  • To identify associations between common pig pathologies using machine learning.
  • To enhance comprehension of the biological links between different swine diseases.
  • To support veterinarians in developing integrated control strategies.

Main Methods:

  • Utilized a multi-dimensional machine learning methodology.
  • Analyzed data from 6,485 batches of slaughtered finishing pigs.

Related Experiment Videos

Last Updated: May 19, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

  • Identified associations among ten typical pathologies.

    • Main Results:

    • Discovered interrelationships between pathologies associated with septicaemia, such as pericarditis and peritonitis, suggesting bacterial challenges (e.g., Haemophilus parasuis, Streptococcus suis).
    • Found hepatic scarring associated with milk spot livers (Ascaris suum) and bacterial pathologies, indicating a potential multi-pathogen cause.
    • Demonstrated the ability of machine learning to uncover complex disease associations.

    Conclusions:

    • Novel machine learning methods offer new insights into pig pathology interrelationships at the batch level.
    • The methodology serves as a powerful tool for hypothesis generation in veterinary research.
    • Applicable to a broad spectrum of veterinary studies for understanding disease dynamics.