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

You might also read

Related Articles

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

Sort by
Same author

Patterns of positron emission mammography uptake by benign and malignant breast lesions: radiopathological correlation with malignant histopathological subtypes, histological grades, and molecular subtypes.

Annals of nuclear medicine·2026
Same author

Association Between Vitamin D Deficiency and Cardiovascular Disease Risk Factors in the MENA Population: A Systematic Review and Meta-Analysis.

Journal of clinical medicine·2026
Same author

ReaderAdaptNet: modeling reader variability in breast imaging with reader-specific embeddings.

Physics in medicine and biology·2026
Same author

Immune Effector Cell-Associated Neurotoxicity Delayed Relapse After Chimeric Antigen Receptor T-Cell Therapy: A Case Report.

Neurology(R) neuroimmunology & neuroinflammation·2025
Same author

Clinical proof of concept of dynamic reconstruction of digital breast tomosynthesis.

Physics in medicine and biology·2025
Same author

Synergistic antifungal activity of lepidium sativum ZnO nanoparticles and nystatin against resistant candida species.

Scientific reports·2025
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 18, 2025

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

42.9K

AI-Based Cancer Detection Model for Contrast-Enhanced Mammography.

Clément Jailin1, Sara Mohamed1, Razvan Iordache1

  • 1GE HealthCare, 283 Rue de la Miniére, 78530 Buc, France.

Bioengineering (Basel, Switzerland)
|August 26, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a deep learning model for contrast-enhanced mammography computer-aided diagnostics (CEM-CAD), significantly improving lesion detection and breast classification performance in cancer diagnosis.

Keywords:
breast cancercancer detectioncomputer aided detectioncontrast-enhanced mammographydeep learning

More Related Videos

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

6.9K
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

22.5K

Related Experiment Videos

Last Updated: Jul 18, 2025

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

42.9K
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

6.9K
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

22.5K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Deep neural networks (DNNs) represent a breakthrough in computer-aided diagnostics (CAD) for breast imaging.
  • Contrast-enhanced mammography (CEM) offers anatomical and functional insights but has limited deep learning (DL) research due to data scarcity.

Purpose of the Study:

  • Develop and evaluate a CEM-CAD system for enhanced lesion detection and breast classification.
  • Address the limited data availability for DL-based CEM analysis.

Main Methods:

  • Optimized a YOLO-based deep learning model trained on a large CEM dataset (1673 patients, 7443 images).
  • Evaluated lesion detection using FROC and breast classification using ROC metrics.
  • Assessed performance with different image inputs and background parenchymal enhancement (BPE) levels.

Main Results:

  • Achieved an AUROC of 0.964 for breast classification.
  • Detecting 90% of cancers with a 0.128 false positive rate per image.
  • Demonstrated superior performance using both low-energy and recombined images, with BPE significantly impacting results.

Conclusions:

  • The developed CEM CAD system demonstrates high performance in lesion detection and breast classification.
  • Its capabilities are comparable to those of experienced radiologists.