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

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...
Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
Computed Tomography01:10

Computed Tomography

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...

You might also read

Related Articles

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

Sort by
Same author

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

Burnout and Work-Life Balance: A Longitudinal Study Into the Transition From Medical School to Postgraduate Training.

Deutsches Arzteblatt international·2026
Same author

Agreement of minimally invasive pulse wave analysis with pulmonary artery and transpulmonary thermodilution cardiac output measurements in perioperative and intensive care medicine: a systematic review and meta-analysis.

British journal of anaesthesia·2026
Same author

[Training conditions in postgraduate family medicine training in Bavaria and the role of the Competence Center: A comparative cross-sectional study].

Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen·2026
Same author

The screening accuracy of the PROMIS® Anxiety measures in adults - A systematic review and multiple-thresholds meta-analysis.

Journal of psychosomatic research·2026

Related Experiment Video

Updated: Jun 3, 2026

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

Image feature evaluation in two new mammography CAD prototypes.

Alexander Hapfelmeier1, Alexander Horsch

  • 1Institute for Medical Statistics and Epidemiology, Technische Universität München, Ismaninger Str. 22, 81675 München, Germany. Alexander.Hapfelmeier@tum.de

International Journal of Computer Assisted Radiology and Surgery
|March 8, 2011
PubMed
Summary
This summary is machine-generated.

This study evaluated computer-aided detection (CADe) and diagnosis (CADx) systems for breast cancer screening. Machine learning approaches generally outperformed knowledge-driven methods, highlighting the importance of proper statistical modeling for accurate results.

More Related Videos

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 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

Related Experiment Videos

Last Updated: Jun 3, 2026

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

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 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

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Biomedical Engineering

Background:

  • Breast cancer remains a significant health concern for adult women, necessitating advancements in early detection and diagnosis.
  • Current computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems require further improvement for enhanced accuracy.
  • Accurate image analysis and feature extraction are critical for effective CAD systems in mammography.

Purpose of the Study:

  • To evaluate the performance of novel CADx and CADe prototypes for breast cancer detection.
  • To compare the effectiveness of 'machine learning' versus 'knowledge-driven' approaches in CAD systems.
  • To provide recommendations for the appropriate application of statistical methods in CAD development and evaluation.

Main Methods:

  • Two CAD prototypes (one CADx, one CADe) were developed, incorporating lesion segmentation, feature extraction, and classification modules.
  • Evaluation utilized the Digital Database for Screening Mammography (DDSM) and a private dataset of digital mammograms.
  • Comparative analysis focused on 'machine learning' and 'knowledge-driven' image analysis strategies, with a detailed discussion of statistical methodologies.

Main Results:

  • The CADx prototype achieved higher classification performance for microcalcifications using machine learning features (AUC=.777) compared to knowledge-driven features (AUC=.657).
  • For mass lesions in the DDSM dataset, the CADx prototype with 242 machine learning features yielded an AUC of .862.
  • The CADe prototype demonstrated high true positive detection rates for mass lesions, with AUC values ranging from .818 to .954 across different feature sets and datasets.

Conclusions:

  • The performance of CAD prototypes is significantly influenced by the appropriate selection and application of statistical models and feature sets.
  • Careful consideration of statistical methods, including feature selection and evaluation schemes, is crucial for reliable CAD system development.
  • Further research and validation are essential to optimize CAD systems for improved breast cancer screening outcomes.