AIMC Topic: Idiopathic Pulmonary Fibrosis
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The Analysis of Proteomics by Machine Learning in Separating Idiopathic Pulmonary Fibrosis from Connective Tissue Disease-Interstitial Lung Disease.
American journal of respiratory and critical care medicine
Aug 15, 2024
Deep Learning-based Segmentation of Computed Tomography Scans Predicts Disease Progression and Mortality in Idiopathic Pulmonary Fibrosis.
American journal of respiratory and critical care medicine
Aug 15, 2024
Despite evidence demonstrating a prognostic role for computed tomography (CT) scans in idiopathic pulmonary fibrosis (IPF), image-based biomarkers are not routinely used in clinical practice or trials. To develop automated imaging biomarkers using ...
Machine Learning of Plasma Proteomics Classifies Diagnosis of Interstitial Lung Disease.
American journal of respiratory and critical care medicine
Aug 15, 2024
Distinguishing connective tissue disease-associated interstitial lung disease (CTD-ILD) from idiopathic pulmonary fibrosis (IPF) can be clinically challenging. To identify proteins that separate and classify patients with CTD-ILD and those with IPF...
Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes.
American journal of respiratory and critical care medicine
May 1, 2024
Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Apply multiple instance learning (MIL) to develop an explainable ...
Deep Learning-based Fibrosis Extent on Computed Tomography Predicts Outcome of Fibrosing Interstitial Lung Disease Independent of Visually Assessed Computed Tomography Pattern.
Annals of the American Thoracic Society
Feb 1, 2024
Radiologic pattern has been shown to predict survival in patients with fibrosing interstitial lung disease. The additional prognostic value of fibrosis extent by quantitative computed tomography (CT) is unknown. We hypothesized that fibrosis extent...
Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis.
Briefings in bioinformatics
Jan 22, 2024
Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous ...
The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis.
Therapeutic advances in respiratory disease
Jan 1, 2024
The review summarizes the applications of CT and AI algorithms for prognostic models in IPF and procedures of model construction. It reveals the current limitations and prospects of AI-aid models, and helps clinicians to recognize the AI algorithms a...
Deep Learning-based Outcome Prediction in Progressive Fibrotic Lung Disease Using High-Resolution Computed Tomography.
American journal of respiratory and critical care medicine
Oct 1, 2022
Reliable outcome prediction in patients with fibrotic lung disease using baseline high-resolution computed tomography (HRCT) data remains challenging. To evaluate the prognostic accuracy of a deep learning algorithm (SOFIA [Systematic Objective Fib...
A machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: A proof-of-concept study.
Medicine
Feb 19, 2021
Fine crackles are frequently heard in patients with interstitial lung diseases (ILDs) and are known as the sensitive indicator for ILDs, although the objective method for analyzing respiratory sounds including fine crackles is not clinically availabl...
Deep Learning of Computed Tomography Virtual Wedge Resection for Prediction of Histologic Usual Interstitial Pneumonitis.
Annals of the American Thoracic Society
Jan 1, 2021
The computed tomography (CT) pattern of definite or probable usual interstitial pneumonia (UIP) can be diagnostic of idiopathic pulmonary fibrosis and may obviate the need for invasive surgical biopsy. Few machine-learning studies have investigated ...
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