AIMC Topic: Idiopathic Pulmonary Fibrosis

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Current State of Fibrotic Interstitial Lung Disease Imaging.

Radiology
Interstitial lung disease (ILD) diagnosis is complex, continuously evolving, and increasingly reliant on thin-section chest CT. Multidisciplinary discussion aided by a thorough radiologic review can achieve a high-confidence diagnosis of ILD in the m...

Identification of genetic indicators linked to immunological infiltration in idiopathic pulmonary fibrosis.

Medicine
This study employed bioinformatics to investigate potential molecular markers associated with idiopathic pulmonary fibrosis (IPF) and examined their correlation with immune-infiltrating cells. Microarray data for IPF were retrieved from the Gene Expr...

From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis.

The British journal of radiology
The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis (IPF), has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable...

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
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
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
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
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
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
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...