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

You might also read

Related Articles

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

Sort by

Same author

Development and application of a liquid chromatography-tandem mass spectrometry method for the analysis of Areca nut alkaloids in rat plasma for a toxicokinetic study.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences·2026

Same author

Functional and Genomic Features of a Lytic Salmonella Phage vB_StyS_KFSST1 for Development as New Feed Additive.

Food science of animal resources·2026

Same author

Multi-omics analysis reveals mitochondrial dysfunction-driven oxidative stress pathways in Caenorhabditis elegans exposed to 2,2-bis(chloromethyl) trimethylene bis[bis(2-chloroethyl) phosphate] (V6).

Journal of pharmaceutical and biomedical analysis·2026

Same author

Binding affinity screening and MAPK/NF-κB mechanisms of antioxidant and anti-inflammatory phenolics compounds from Gynura procumbens stem extract (GPSE).

Scientific reports·2026

Same author

Large Language Model-Based Simplification of Digital Therapeutics Explanations for Insomnia and Nicotine Dependence: Two Randomized Online Experiments.

JMIR human factors·2026

Same author

Quantitative analysis of novel brominated flame retardants using multilayer silica/Florisil purification coupled with GC-MS/MS and assessment of their levels in vegetables by plant parts.

Environmental pollution (Barking, Essex : 1987)·2026

Search research articles

Related Experiment Video

Updated: Oct 8, 2025

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

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

Published on: December 19, 2020

Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography

Hyung Min Kim¹, Taehoon Ko², In Young Choi¹

¹Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea.

International Journal of Medical Informatics

|December 24, 2021

Summary

A new algorithm combining lung segmentation and deep learning accurately diagnoses asbestosis from CT scans, outperforming radiologists. This tool aids early detection and clinical decision support for this lung disease.

Keywords:

Asbestosis Classification Clinical decision support system Computed tomography Long-term recurrent convolutional network Segmentation

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

Related Experiment Videos

Last Updated: Oct 8, 2025

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

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

Published on: December 19, 2020

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

Area of Science:

Medical Imaging
Artificial Intelligence
Pulmonology

Background:

Early detection of asbestosis is crucial for effective management.
Accurate and rapid diagnostic tools are needed for asbestosis.
Computed tomography (CT) imaging is a key modality for lung disease diagnosis.

Purpose of the Study:

To develop an algorithm for diagnosing asbestosis using CT images.
To integrate lung segmentation and deep learning for a clinical decision support system (CDSS).
To enhance the accuracy and efficiency of asbestosis diagnosis.

Main Methods:

Lung segmentation was performed on CT images using a threshold-based method.
A deep learning model (long-term recurrent convolutional network) classified segmented lungs as asbestosis or normal.
Performance was assessed using area under the receiver operating characteristic curve (AUROC) and F1 score.

Main Results:

The algorithm, utilizing a DenseNet201 model, achieved high performance metrics.
Sensitivity: 0.962, Specificity: 0.975, Accuracy: 0.970, AUROC: 0.968, F1 Score: 0.961.
The algorithm demonstrated superior diagnostic accuracy compared to human radiologists.

Conclusions:

A novel algorithm was developed for asbestosis diagnosis with high accuracy.
The algorithm significantly outperformed radiologists in diagnostic accuracy (0.970 vs. 0.73-0.79).
The developed algorithm is a promising tool for clinical decision support in asbestosis diagnosis via CT.