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

Artificial Intelligence-Based System for Detecting Attention Levels in Students06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

5.3K
This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more...
5.3K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System05:33

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

811
Integrated image management, artificial intelligence (AI), and reporting systems have revolutionized diagnostic pathology practice. In this paper, we introduce FlexLIS, a state-of-the-art system that enables AI to assist pathologists in performing histopathology image assessments and generating diagnostic reports.
811
Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

1.4K
A process of registering cone-beam computed tomography scans and digital dental images has been presented using artificial intelligence (AI) -assisted identification of landmarks and merging. A comparison with surface-based registration shows that AI-based digitization and integration are reliable and...
1.4K
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

2.7K
The micronucleus (MN) assay is a well-established test for quantifying DNA damage. However, scoring the assay using conventional techniques such as manual microscopy or feature-based image analysis is laborious and challenging. This paper describes the methodology to develop an artificial intelligence model to score the MN assay using imaging flow cytometry...
2.7K
Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

4.5K
This protocol describes a high-throughput workflow for artificial intelligence-driven segmentation of pathology-confirmed regions of interest from stained, thin tissue section images for enrichment of histology-resolved cell populations using laser microdissection. This strategy includes a novel algorithm enabling the transfer of demarcations denoting cell populations of interest directly to laser...
4.5K
Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

17.6K
Every min counts in acute stroke care. This guide shows how to establish a Stroke Team algorithm and enhance its performance with regular simulation training. The principles of Crew Resource Management (CRM) facilitate a straight workflow, reduce door-to-needle times and increase staff...
17.6K

You might also read

Related Articles

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

Sort by
Same author

AI-Based Myocardial Segmentation and Attenuation Mapping Improved Detection of Myocardial Ischemia and Infarction on Emergency CT Angiography.

Bioengineering (Basel, Switzerland)·2026
Same author

Impact of CT dose on AI performance: A comparison of radiomics, deep, and foundation models in a multicentric anthropomorphic phantom study.

Medical physics·2026
Same author

Automated detection of gallbladder stones using a deep learning algorithm on computed tomography scans.

Abdominal radiology (New York)·2026
Same author

Longitudinal analysis of psychometric properties of the Seattle Angina Questionnaire among patients who underwent coronary artery bypass grafting in Serbia.

Population health metrics·2025
Same author

Liver Segment and Lesion Segmentation on CT and MRI: An Open-Source Contribution to TotalSegmentator.

Journal of imaging informatics in medicine·2025
Same author

Correlation of BIPQ score with socioeconomic characteristics of patients with COVID-19 pneumonia and CT severity score.

Journal of infection in developing countries·2025

Related Experiment Video

Updated: Jan 19, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K

A Practical Guide to Artificial Intelligence-Based Image Analysis in Radiology.

Thomas Weikert1, Joshy Cyriac, Shan Yang

  • 1From the Department of Radiology, University Hospital Basel, Basel, Switzerland.

Investigative Radiology
|September 11, 2019
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers powerful image analysis for radiology. This review guides AI project implementation and critical appraisal of AI software in radiology departments.

More Related Videos

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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

Published on: July 11, 2025

811
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.4K

Related Experiment Videos

Last Updated: Jan 19, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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

Published on: July 11, 2025

811
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.4K

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is increasingly evaluated for medical image analysis in radiology.
  • Current AI methods are often developed for non-medical data, and radiology data structures are not yet optimized for AI.
  • Implementing AI in radiology presents significant challenges due to data and methodological mismatches.

Purpose of the Study:

  • To provide a comprehensive guide to the AI project pipeline for automated image analysis in radiology.
  • To encourage the adoption and implementation of AI tools within radiology departments.
  • To equip readers with the skills to critically evaluate AI-based software used in radiological practice.

Main Methods:

  • Review of current literature and best practices in AI development and implementation for medical imaging.
  • Structured overview of the AI project lifecycle, from data preparation to deployment and evaluation.
  • Guidance on assessing the technical and clinical validity of AI applications in radiology.

Main Results:

  • AI offers significant potential for enhancing image analysis in radiology.
  • Successful AI implementation requires addressing data readiness and adapting AI methodologies.
  • A structured approach is necessary for developing and validating AI tools for radiology.

Conclusions:

  • AI has the potential to revolutionize image analysis in radiology.
  • Overcoming implementation barriers through guided project pipelines is crucial for AI adoption.
  • Critical appraisal skills are essential for radiologists to effectively utilize AI-based software.