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

Ultrasonography01:17

Ultrasonography

8.2K
Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
8.2K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

492
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...
492
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

1.4K
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Network-based machine learning to identify biomarkers for systemic lupus erythematosus.

BMC biology·2026
Same author

MammoDenseSegNet: A Context-Aware Deep Learning Model for Dense Tissue Segmentation in Digital Mammograms.

Journal of imaging informatics in medicine·2026
Same author

Adnexal torsion diagnosis framework with CT-based adaptive preprocessing and deep neural networks.

Scientific reports·2026
Same author

Anthracotic Right Supraclavicular Lymph Node Mimicking Metastasis on FDG-PET/CT in Endometrial Carcinosarcoma: A Case Report.

In vivo (Athens, Greece)·2026
Same author

Counterfactual Reasoning for Mammogram Classification via Semantic Texture Masking.

Journal of imaging informatics in medicine·2026
Same author

A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer.

Cell reports methods·2026

Related Experiment Video

Updated: Mar 2, 2026

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

1.7K

Lack of agreement between radiologists: implications for image-based model observers.

Juhun Lee1, Robert M Nishikawa1, Ingrid Reiser2

  • 1University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|May 12, 2017
PubMed
Summary

Radiologists disagreed on the best breast CT image reconstructions for cancer diagnosis, preferring images that did not align with their actual diagnostic performance. This highlights challenges in creating universal AI models for image analysis.

Keywords:
breast cancerbreast computed tomographydiagnostic performancemodel observersreader study

More Related Videos

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

11.9K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.8K

Related Experiment Videos

Last Updated: Mar 2, 2026

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

1.7K
Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

11.9K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.8K

Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Iterative image reconstruction (IIR) algorithms generate diverse breast computed tomography (CT) image appearances.
  • Assessing diagnostic performance and subjective image quality is crucial for optimizing imaging protocols.

Purpose of the Study:

  • To evaluate radiologist agreement on breast CT image reconstruction preferences.
  • To compare subjective image quality assessments with objective diagnostic performance.
  • To explore the feasibility of developing a representative model observer for radiologist performance.

Main Methods:

  • Six experienced radiologists assessed 24 reconstructions from 102 breast CT cases (62 malignant, 40 benign).
  • Reconstructions varied in image quality (smooth/low-noise to sharp/high-noise).
  • Radiologists ranked reconstructions by preference and rated malignancy likelihood.

Main Results:

  • Significant disagreement existed among radiologists regarding optimal diagnostic reconstructions.
  • Radiologists preferred mid-sharp/noise images, but these did not correlate with the highest diagnostic performance (AUCs 0.62-0.96).
  • Preferred reconstructions did not consistently yield the best classification results.

Conclusions:

  • Radiologist preferences for breast CT image reconstruction are subjective and do not always align with diagnostic accuracy.
  • Inter-reader variability poses challenges for developing a single, representative model observer for AI-driven image analysis.
  • Further research is needed to reconcile subjective preferences with objective performance metrics in medical imaging.