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 Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

1.3K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.3K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.0K
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
1.0K
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

872
The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
872
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

8.8K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
8.8K
X-ray Imaging01:24

X-ray Imaging

9.5K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
9.5K
Computed Tomography01:10

Computed Tomography

7.8K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Disparate privacy risks from medical AI.

Nature·2026
Same author

Correction to: Reducing Domestic Wood Burning through Voluntary Air Quality Alerts: An IBM-WASH Evaluation of a Pilot Intervention in Wales.

Environmental management·2026
Same author

Reducing Domestic Wood Burning through Voluntary Air Quality Alerts: An IBM-WASH Evaluation of a Pilot Intervention in Wales.

Environmental management·2026
Same author

Case of Atypical Dandy-Walker Variant in Adult with Refractory Headaches.

Neurology India·2025
Same author

Safeguarding children on Welsh roads: the 20 mph policy and the road ahead.

BMJ paediatrics open·2025
Same author

The UK consensus supporting effective introduction of novel treatments for multiple myeloma in the National Health Service.

EJHaem·2024
Same journal

Unlocking the capacity of Mn-based Prussian blue cathodes in capacitive deionization.

Nature communications·2026
Same journal

Scaling biodiversity-stability relationships from populations to meta-communities across trophic levels.

Nature communications·2026
Same journal

Thermodynamically programmed one-pot CRISPR platform for point-of-care SNP genotyping.

Nature communications·2026
Same journal

Engineering all-organic electrocatalysts with asymmetric dual-active sites for uncommon oxygen-evolving pathway.

Nature communications·2026
Same journal

Rapid GC content evolution in rice through GC-biased gene conversion and selection for translation efficiency.

Nature communications·2026
Same journal

Declines in organic matter persistence with increased soil carbon.

Nature communications·2026
See all related articles

Related Experiment Video

Updated: Dec 14, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K

Causality matters in medical imaging.

Daniel C Castro1, Ian Walker2, Ben Glocker3

  • 1Biomedical Image Analysis Group, Department of Computing, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK. dc315@imperial.ac.uk.

Nature Communications
|July 24, 2020
PubMed
Summary
This summary is machine-generated.

Causal reasoning addresses key machine learning challenges in medical imaging, such as limited data and dataset discrepancies. This approach enhances transparency in data handling and bias mitigation for improved model performance.

More Related Videos

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.6K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

8.2K

Related Experiment Videos

Last Updated: Dec 14, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.6K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

8.2K

Area of Science:

  • Machine Learning
  • Medical Imaging
  • Causal Inference

Background:

  • Machine learning in medical imaging faces challenges like data scarcity and domain shift.
  • Existing methods often lack transparency in addressing these issues.

Purpose of the Study:

  • To apply causal reasoning to machine learning in medical imaging.
  • To provide a framework for transparent decision-making and bias mitigation.

Main Methods:

  • Utilizing causal inference principles to analyze medical imaging datasets.
  • Categorizing potential biases and proposing mitigation strategies.
  • Illustrating with clinical examples.

Main Results:

  • Causal reasoning offers a transparent approach to data collection, annotation, preprocessing, and learning.
  • Identified and categorized biases and mitigation techniques.
  • Demonstrated the importance of causal relationships between images and annotations.

Conclusions:

  • Causal reasoning is crucial for robust machine learning in medical imaging.
  • Recommends step-by-step guidelines for future research and development.