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

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.6K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.6K

You might also read

Related Articles

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

Sort by
Same author

A comparative study of computer vision models for oral cancer detection from oral photographs.

Computer methods and programs in biomedicine·2025
Same author

Integrating clinical indications and patient demographics for multilabel abnormality classification and automated report generation in 3D chest CT scans.

Frontiers in radiology·2025
Same author

Urinary tract endometriosis: Revisiting the definition of ureterolysis.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics·2025
Same author

The Radiologist at the Forefront of Management of Ovarian and Adnexal Lesions.

Radiology·2024
Same author

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution.

Diagnostic and interventional imaging·2024
Same author

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.

Insights into imaging·2024
Same journal

How to optimise breast cancer staging with contrast-enhanced mammography: current evidence and clinical implications.

Insights into imaging·2026
Same journal

MRI-based quantification of intratumoral heterogeneity for predicting recurrence risk in ER+/HER2- breast cancer.

Insights into imaging·2026
Same journal

Innovative competency-based education approach to radiology residency: integrating modular training in clinical practice and research.

Insights into imaging·2026
Same journal

Development and validation of an interpretable ultrasound radiomics model for benign and malignant classification of breast lesions: a multicenter large-sample study.

Insights into imaging·2026
Same journal

Wrist arthro-CT: don't forget to check the foveal attachment.

Insights into imaging·2026
Same journal

Reduced environmental impact in body CT imaging with deep learning reconstruction: experience of a high-volume tertiary referral center.

Insights into imaging·2026
See all related articles

Related Experiment Video

Updated: Feb 17, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.8K

Transforming a clinical study database into a structured database adapted to artificial intelligence applications.

Thibault Sauron1, Carole Lazarus2, Camille Kurtz1

  • 1LIPADE, Université Paris Cité, Paris, France.

Insights Into Imaging
|February 15, 2026
PubMed
Summary
This summary is machine-generated.

We developed a curation methodology to adapt clinical trial MRI data for training artificial intelligence models. This framework enhances the secondary use of high-quality health data for developing AI imaging tools.

Keywords:
Artificial intelligenceClinical trialData curationMRIMedical computer vision

More Related Videos

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

5.4K
Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
11:18

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

16.5K

Related Experiment Videos

Last Updated: Feb 17, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.8K
TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

5.4K
Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
11:18

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

16.5K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Clinical Trials

Background:

  • Medical imaging databases for training AI are scarce.
  • Clinical trial databases offer high-quality annotated data but are not AI-ready.
  • Developing AI tools for healthcare requires suitable datasets.

Purpose of the Study:

  • To develop a methodology and tools for curating clinical trial databases for AI training and testing.
  • To adapt existing clinical trial data for secondary use in AI development.
  • To create a framework for the efficient use of annotated medical imaging data.

Main Methods:

  • Utilized MRIs from the EURAD clinical trial.
  • Defined inclusion/exclusion criteria and applied the principle of parsimony.
  • Implemented quality control through automated and manual checks, harmonized DICOM fields and sequence names.

Main Results:

  • Curated a database from 713 patients.
  • Reduced directory structure complexity by 44% and file count by 95%.
  • Identified 62 essential DICOM fields for AI applications and harmonized sequence names.

Conclusions:

  • A methodology was established to build AI-ready databases from clinical trial data.
  • Highlights the need for a systematic framework for secondary health data use in AI.
  • Shared open-source tools and methodology for AI model development.