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Idiopathic Pulmonary Fibrosis

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A three-gene random forest model for diagnosing idiopathic pulmonary fibrosis based on circadian rhythm-related genes in lung tissue.

Expert review of respiratory medicine
BACKGROUND: The disorder of circadian rhythm could be a key factor mediating fibrotic lung disease Therefore, our study aims to determine the diagnostic value of circadian rhythm-related genes (CRRGs) in IPF.

Anti-mutated citrullinated vimentin antibodies are increased in IPF patients.

Respiratory medicine and research
INTRO: An increased prevalence of serum anti-MCV antibody is observed in the serum of patients with idiopathic pulmonary fibrosis (IPF) but the clinical relevance of these antibodies is unknown.

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease.

PloS one
To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected...

A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia.

Chest
BACKGROUND: Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed.

Development and validation of a CT-based deep learning algorithm to augment non-invasive diagnosis of idiopathic pulmonary fibrosis.

Respiratory medicine
RATIONALE: Non-invasive diagnosis of idiopathic pulmonary fibrosis (IPF) involves identification of usual interstitial pneumonia (UIP) pattern by computed tomography (CT) and exclusion of other known etiologies of interstitial lung disease (ILD). How...

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

European radiology
OBJECTIVES: To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs.

Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis.

Briefings in bioinformatics
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 ...

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
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...

Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes.

American journal of respiratory and critical care medicine
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 ...

Machine Learning of Plasma Proteomics Classifies Diagnosis of Interstitial Lung Disease.

American journal of respiratory and critical care medicine
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...