Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological Investigation III: Pulmonary Angiogram and PET Scan

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...

Radiological Investigation II: MRI and Ventilation Perfusion Scan

Radiological Investigation II: MRI and Ventilation Perfusion Scan

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...

Radiological Investigation I: X-ray and CT

Radiological Investigation I: X-ray and CT

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...

You might also read

Related Articles

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

Sort by

Same author

Intelligent Head and Neck CTA Report Quality Detection with Large Language Models.

Journal of imaging informatics in medicine·2025

Same author

Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study.

Journal of medical Internet research·2022

Same author

Sequential Data-Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development.

Journal of medical Internet research·2022

Same author

Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study.

JMIR medical informatics·2021

Same author

[Effects of electroacupuncture at "Neiguan" (PC 6) on p38 MAPK signaling pathway in rats with cardiac hypertrophy].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion·2012

Same author

Accurate measurement of oxygen consumption in children undergoing cardiac catheterization.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions·2012

Search research articles

Related Experiment Video

Updated: Sep 7, 2025

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

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

Published on: May 19, 2023

Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing.

Xiaolu Fei¹, Pengyu Chen¹, Lan Wei¹

¹Information Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.

Bioengineering (Basel, Switzerland)

|June 23, 2022

Summary

This study demonstrates that a Natural Language Processing (NLP) model can automatically generate accurate pulmonary nodule follow-up recommendations from radiology reports, improving efficiency and quality management.

Keywords:

knowledge graph natural language processing pulmonary nodule quality management radiology report

More Related Videos

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

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

Published on: October 13, 2023

$Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy$

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

Related Experiment Videos

Last Updated: Sep 7, 2025

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

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

Published on: May 19, 2023

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

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

Published on: October 13, 2023

$Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy$

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

Area of Science:

Medical Informatics
Artificial Intelligence in Radiology
Natural Language Processing

Background:

Radiology reports contain unstructured findings crucial for pulmonary nodule management.
Manual review of these findings for follow-up recommendations is time-consuming and prone to variability.
Automating this process can enhance efficiency and ensure guideline adherence.

Purpose of the Study:

To assess the feasibility of an automated system for generating follow-up recommendations based on pulmonary nodule radiology reports.
To develop and evaluate a Natural Language Processing (NLP) model for extracting key information and generating recommendations.
To compare automated recommendations with existing clinical practice for accuracy and appropriateness.

Main Methods:

A Natural Language Processing (NLP) model was developed using deep learning and conditional random-field algorithms to process unstructured findings from 48,091 pulmonary nodule radiology reports.
An information extraction model identified eight types of entities with high accuracy.
A knowledge graph, incorporating Fleischner Society guidelines, was constructed to generate automated follow-up recommendations using rule templates.

Main Results:

The NLP model achieved a recognition accuracy of up to 94.22% for identified entities.
Automated follow-up recommendations were generated for 43,898 reports, achieving a matching rate of 91.28% against the impression section.
The system demonstrated high accuracy in generating appropriate follow-up suggestions.

Conclusions:

Natural Language Processing (NLP) is effective for extracting structured information from Chinese radiology reports concerning pulmonary nodules.
The developed NLP model and knowledge graph can automatically generate timely and intelligent follow-up recommendations.
This approach offers potential for improved post-quality management of follow-up recommendations in clinical practice.