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

318
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...
318
Assessment of Airway, Skin Color, and Use of Accessory Muscles01:30

Assessment of Airway, Skin Color, and Use of Accessory Muscles

1.1K
A thorough assessment of respiratory health is paramount in clinical settings to identify and manage respiratory distress and ensure adequate oxygenation. This article elaborates on the critical aspects of respiratory evaluation, including airway assessment, skin color examination, and the observation of accessory muscle use, which are integral to effectively diagnosing and managing patients with respiratory conditions.
Introduction
The initial evaluation of a patient's respiratory system...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Discharge Practices After Hospitalization for COPD Exacerbations: A Physician Survey and SWOT Analysis.

Healthcare (Basel, Switzerland)·2026
Same author

Quantification Beyond Binary of MR FLAIR Hyperintensity Lesions in Acute Ischemic Stroke of Unknown Time Since Onset.

Diagnostics (Basel, Switzerland)·2026
Same author

Ultrasound-Based Techniques for Visualization of Dermal Microvasculature: A Scoping Review.

Diagnostics (Basel, Switzerland)·2026
Same author

<sup>64</sup>Cu-DOTATATE-PET/CT in Neuroborreliosis Shows Increased Tracer Uptake in Dorsal Root and Paravertebral Ganglia.

Diagnostics (Basel, Switzerland)·2026
Same author

Healthcare professionals' experiences of supporting pregnant women with mental illness with their smoking behaviours: A qualitative study.

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco·2026
Same author

Estimation of metabolic water production in human and rat brain and spinal cord.

Fluids and barriers of the CNS·2026

Related Experiment Video

Updated: Aug 5, 2025

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

14.2K

Performance and Agreement When Annotating Chest X-ray Text Reports-A Preliminary Step in the Development of a Deep

Dana Li1,2, Lea Marie Pehrson1,3, Rasmus Bonnevie4

  • 1Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark.

Diagnostics (Basel, Switzerland)
|March 29, 2023
PubMed
Summary

Chest X-ray reports are vital for artificial intelligence (AI) decision support systems. Annotations by non-radiologists with general knowledge can align well with expert radiologists, especially when trained radiologists are unavailable.

Keywords:
agreementartificial intelligencechest X-raydatadeep learningdevelopmentperformanceradiologiststext annotation

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

563

Related Experiment Videos

Last Updated: Aug 5, 2025

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

14.2K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

563

Area of Science:

  • Radiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Chest X-ray reports serve as crucial communication tools and data sources for developing AI-driven decision support systems.
  • Consistent understanding and accurate labeling of these reports are essential for reliable AI model training.

Purpose of the Study:

  • To evaluate how different annotators comprehend and label chest X-ray reports.
  • To assess the performance of various medical professionals in annotating these reports for AI development.

Main Methods:

  • 200 chest X-ray reports were annotated by a diverse group: board-certified radiologists, trained radiologists, radiographers, a non-radiological physician, and a medical student.
  • Consensus labels from experienced radiologists defined the 'gold standard'.
  • Matthew's Correlation Coefficient (MCC) and descriptive statistics were used to measure annotation performance and agreement.

Main Results:

  • Intermediate radiologists achieved the highest correlation with the gold standard (MCC 0.77).
  • Novice radiologists and medical students showed strong performance (MCC 0.71).
  • Non-radiological annotators with general knowledge (physician, student) demonstrated better alignment with radiologists than specialized non-radiologists.

Conclusions:

  • For AI development using chest X-ray reports, non-radiological annotators with general medical knowledge can provide valuable input when expert radiologists are limited.
  • The findings suggest that generalist annotators may be more suitable than sub-specialized non-radiologists for certain AI training tasks in diagnostic radiology.