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

Positron Emission Tomography01:29

Positron Emission Tomography

6.3K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
6.3K
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

517
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...
517
Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

204
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...
204
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

266
Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
266
Computed Tomography01:10

Computed Tomography

7.3K
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...
7.3K
Classification of Illness01:17

Classification of Illness

8.1K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.1K

You might also read

Related Articles

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

Sort by
Same author

AI for Radiology: A Primer Part II. Interacting with AI Results.

Radiology·2026
Same author

Evaluating the Performance of Intraoperative Ultrasound in Treating Pituitary Adenomas Through the Endoscopic Endonasal Route.

Operative neurosurgery (Hagerstown, Md.)·2026
Same author

Postdeployment Monitoring and Surveillance Methods, Guidelines, and Possibilities for AI in Radiology.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same author

Radiology Reimagined: Interoperability and Lessons Learned from the Imaging AI in Practice Demonstration.

Radiology·2026
Same author

Reporting checklist for foundation and large language models in medical research (REFINE): an international consensus guideline.

Diagnostic and interventional radiology (Ankara, Turkey)·2026
Same author

Open-Source Dataset for the RSNA Screening Mammography Cancer Detection Challenge.

Radiology. Artificial intelligence·2026

Related Experiment Video

Updated: Oct 25, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.1K

Natural Language Processing of Radiology Text Reports: Interactive Text Classification.

Walter F Wiggins1, Felipe Kitamura1, Igor Santos1

  • 1Department of Radiology, Duke University Health System, Duke University Hospital, Box 3808, 2301 Erwin Rd, Durham, NC 27710 (W.F.W.); Department of Diagnostic Imaging, Universidade Federal de São Paulo, Escola Paulista de Medicina, São Paulo, Brazil (F.K., I.S.); Head of AI, Diagnósticos da América SA (DASA), São Paulo, Brazil (F.K.); FIDI, NESS Health, São Paulo, Brazil (I.S.); and Department of Radiology, Ohio State University, Columbus, Ohio (L.M.P.).

Radiology. Artificial Intelligence
|August 5, 2021
PubMed
Summary

This study introduces natural language processing (NLP) for radiology reports using deep neural networks. The accessible Google Colab notebook helps classify chest X-ray reports as normal or abnormal.

Keywords:
Computer ApplicationsInformaticsNatural Language ProcessingNegative Expression RecognitionNeural Networks

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

442
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

1.7K

Related Experiment Videos

Last Updated: Oct 25, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.1K
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

442
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

1.7K

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Radiology

Background:

  • Natural Language Processing (NLP) is crucial for analyzing unstructured clinical data like radiology reports.
  • Deep neural networks offer advanced capabilities for text analysis and classification in healthcare.

Purpose of the Study:

  • To provide a hands-on introduction to NLP techniques for radiology report analysis.
  • To demonstrate the application of deep neural networks for classifying chest X-ray reports.
  • To create an accessible learning module for individuals with limited programming experience.

Main Methods:

  • Utilized a corpus of radiology reports from the Indiana University chest x-ray collection.
  • Developed a Google Colaboratory notebook with hidden code for guided learning.
  • Implemented NLP concepts including tokenization, numericalization, language modeling, and word embeddings.
  • Trained a deep NLP model to classify reports as normal or abnormal.

Main Results:

  • Successfully guided learners through data exploration, preparation, and model training.
  • Demonstrated the feasibility of using deep NLP models for automated report classification.
  • Provided an interactive and self-guided learning experience for NLP in radiology.

Conclusions:

  • The developed module effectively introduces NLP and deep neural networks for radiology report analysis.
  • Accessible tools like Google Colab can facilitate broader adoption of NLP in medical informatics.
  • Automated classification of radiology reports holds potential for improving clinical workflows.