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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...

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Related Experiment Video

Updated: Jul 7, 2025

A Multimodal Imaging Approach Based on Micro-CT and Fluorescence Molecular Tomography for Longitudinal Assessment of Bleomycin-Induced Lung Fibrosis in Mice

A Multimodal Imaging Approach Based on Micro-CT and Fluorescence Molecular Tomography for Longitudinal Assessment of Bleomycin-Induced Lung Fibrosis in Mice

Published on: April 13, 2018

Developing radiology diagnostic tools for pulmonary fibrosis using machine learning methods.

Weijia Fan¹, Qixuan Chen¹, Valerie Maccarrone²

¹Department of Biostatistics, Mailman School of Public Health Columbia University, 722 st 168th Street, New York, NY 10032, United States of America.

Clinical Imaging

|December 23, 2023

Summary

This summary is machine-generated.

Machine learning aids in diagnosing pulmonary fibrosis patterns by identifying key radiographic features. An online app using Bayesian additive regression trees assists radiologists, improving diagnostic accuracy.

Keywords:

Bayesian additive regression tree Classification and regression tree Diagnostic tool Machine learning Online implementation tool Pulmonary fibrosis

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

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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

Area of Science:

Radiology
Artificial Intelligence
Pulmonary Medicine

Background:

Accurate diagnosis of pulmonary fibrosis patterns is crucial for patient management.
Radiographic characteristics of different fibrosis patterns often overlap, posing diagnostic challenges.

Purpose of the Study:

To identify key radiographic features for diagnosing common pulmonary fibrosis patterns using machine learning.
To develop a user-friendly online diagnostic application for pulmonary fibrosis pattern recognition.

Main Methods:

Retrospective chart review of 400 patients with pulmonary fibrosis.
Application of classification and regression tree (CART) and Bayesian additive regression tree (BART) machine learning models.
Development of an online diagnostic app integrating the BART model.

Main Results:

Four critical radiographic features identified: peripheral distribution, homogeneity, lower lobe predominance, and mosaic attenuation.
BART outperformed CART in diagnostic prediction accuracy.
BART provided predicted probabilities with uncertainty intervals for each diagnosis.

Conclusions:

The BART model and associated app serve as effective tools for radiologists.
The developed tool assists in the accurate recognition of pulmonary fibrosis patterns.
Improved diagnostic support for pulmonary fibrosis pattern identification.