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

Computed Tomography01:10

Computed Tomography

4.7K
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...
4.7K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

34
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
34

You might also read

Related Articles

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

Sort by
Same author

Refining Stratification in Premenopausal Node-Positive HR<sup>+</sup>/HER2<sup>-</sup> Breast Cancer With a Novel AI-Based Test.

Clinical breast cancer·2026
Same author

MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.

npj drug discovery·2026
Same author

When One Sequence Is Enough-And When It Isn't.

Radiology. Artificial intelligence·2026
Same author

Transplant-ready? Evaluating AI lung segmentation models in candidates with severe lung disease.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

Computational Morphometry of Peripheral Nerves: A Pipeline Perspective on Reproducibility and Generalization.

Neuroinformatics·2026
Same author

Understanding Transformer-Based Classifications of Medical Text Using a Large Language Model for the Attribution of Feature Importance: Proof-of-Concept Algorithm Development and Validation Study.

JMIR medical informatics·2026

Related Experiment Video

Updated: Aug 9, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K

A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast

Nicholas Konz1, Mateusz Buda2,3, Hanxue Gu1

  • 1Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina.

JAMA Network Open
|February 23, 2023
PubMed
Summary

An international AI challenge developed high-sensitivity algorithms for detecting cancer in digital breast tomosynthesis (DBT) images. This effort established benchmarks and shared resources to advance AI in breast cancer diagnostics.

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:30

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

148

Related Experiment Videos

Last Updated: Aug 9, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:30

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

148

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate artificial intelligence (AI) for digital breast tomosynthesis (DBT) can enhance cancer detection and reduce global healthcare costs.
  • Developing AI for DBT analysis requires accessible training data, clear benchmarks, and shared code.

Purpose of the Study:

  • To create a benchmark for AI algorithms detecting lesions in DBT.
  • To make training and evaluation data publicly available.
  • To release code for existing AI methods in DBT analysis.

Main Methods:

  • A multi-institutional international grand challenge was conducted.
  • Research teams developed AI algorithms to detect lesions in DBT.
  • A dataset of 22,032 DBT volumes was provided for algorithm development and testing across two phases.

Main Results:

  • Eight teams participated, developing AI algorithms for DBT lesion detection.
  • The top-performing team achieved a mean sensitivity of 0.957 for biopsied lesions.
  • Aggregated mean sensitivity across all algorithms was 0.879, and 0.926 for Phase 2 participants.

Conclusions:

  • An international competition yielded AI algorithms with high sensitivity for detecting DBT lesions.
  • A standardized performance benchmark and publicly available data/code were released.
  • These resources aim to accelerate future research in computer-assisted DBT diagnosis.