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

Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

219
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...
219
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

160
The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
160
Computed Tomography01:10

Computed Tomography

4.4K
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...
4.4K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

208
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
208
X-ray Imaging01:24

X-ray Imaging

5.4K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
5.4K
Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

88
Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
88

You might also read

Related Articles

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

Sort by
Same author

A nationwide survey of medical resource usage for cancer treatment using Japanese health claims data from 2011 to 2022.

Scientific reports·2026
Same author

Exploring Factors Influencing Nursing Task Prioritization for Supportive Information System Design: Qualitative Study With Thematic Analysis.

JMIR human factors·2026
Same author

Clinical Barriers to Hands-Free, Eyes-Free Voice Input for Nursing Records: Field Usability Study.

Asian/Pacific Island nursing journal·2026
Same author

Assessing Clinical Decision-Making Aided by a RAG-Based Dialog in Pre-Examination on Non-Odontogenic Tooth Pain.

Studies in health technology and informatics·2026
Same author

Development and Pilot Evaluation of the Full-Cloud Personal Health Train for Secure Federated Analysis Across Clinical Research Core Hospitals in Japan.

Studies in health technology and informatics·2026
Same author

Automatic selection of optical coherence tomography images for prognostic prediction models in age-related macular degeneration.

Computer methods and programs in biomedicine·2026

Related Experiment Video

Updated: Jun 15, 2025

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

13.2K

Investigation of Radiologist Diagnostic Difficulty Prediction Without CT Images.

Kazumasa Kishimoto1, Masahiro Yakami1, Tomohiro Kuroda1

  • 1Kyoto University, Japan.

Studies in Health Technology and Informatics
|August 23, 2024
PubMed
Summary

This study introduces a new method to predict the difficulty of interpreting medical images for radiologists. The approach uses patient data and order information, achieving 70% accuracy without analyzing the images themselves.

Keywords:
Deep learningclassificationdiagnosisdifficultymultimodal

More Related Videos

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.1K
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.8K

Related Experiment Videos

Last Updated: Jun 15, 2025

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

13.2K
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.1K
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.8K

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Clinical Workflow Optimization

Background:

  • Accurate evaluation of radiologist interpretation workload is crucial for effective collaboration.
  • Assessing the difficulty of individual radiology cases objectively remains a significant challenge.
  • Current methods often lack the granularity to account for case-specific interpretation complexity.

Purpose of the Study:

  • To develop and validate a multimodal classifier for predicting the interpretation difficulty of radiology cases.
  • To assess the feasibility of predicting case difficulty using non-imaging data.
  • To improve workload distribution and efficiency in radiology departments.

Main Methods:

  • A multimodal classifier was developed using structural and textual patient data.
  • The model utilized order information and patient demographics, excluding medical images.
  • Performance was evaluated using standard classification metrics.

Main Results:

  • The proposed classifier achieved a specificity of 0.9.
  • The overall accuracy of the difficulty prediction model was 0.7.
  • The model demonstrated the potential to predict case difficulty without direct image analysis.

Conclusions:

  • Predicting radiology case difficulty using multimodal, non-imaging data is feasible.
  • This approach can aid in objective workload evaluation and management for radiologists.
  • Further research can refine the model for enhanced clinical applicability and collaboration.