AIMC Topic: Lung Diseases, Interstitial

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Impact of Photon-counting Detector Computed Tomography on a Quantitative Interstitial Lung Disease Machine Learning Model.

Journal of thoracic imaging
PURPOSE: Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model.

Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients.

Respiratory research
BACKGROUND: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are need...

Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology.

Respiratory research
BACKGROUND: The composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI...

Diagnosis of Fibrotic Interstitial Lung Diseases Based on the Combination of Label-Free Quantitative Multiphoton Fiber Histology and Machine Learning.

Laboratory investigation; a journal of technical methods and pathology
Interstitial lung disease (ILD), characterized by inflammation and fibrosis, often suffers from low diagnostic accuracy and consistency. Traditional hematoxylin and eosin (H&E) staining primarily reveals cellular inflammation with limited detail on f...

Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts.

Scientific reports
Visual scoring of interstitial lung disease in systemic sclerosis (SSc-ILD) from CT scans is laborious, subjective and time-consuming. This study aims to develop a deep learning framework to automate SSc-ILD scoring. The automated framework is a casc...

Artificial intelligence-based quantification of pulmonary HRCT (AIqpHRCT) for the evaluation of interstitial lung disease in patients with inflammatory rheumatic diseases.

Rheumatology international
High-resolution computed tomography (HRCT) is important for diagnosing interstitial lung disease (ILD) in inflammatory rheumatic disease (IRD) patients. However, visual ILD assessment via HRCT often has high inter-reader variability. Artificial intel...

Use of artificial intelligence algorithms to analyse systemic sclerosis-interstitial lung disease imaging features.

Rheumatology international
The use of artificial intelligence (AI) in high-resolution computed tomography (HRCT) for diagnosing systemic sclerosis-associated interstitial lung disease (SSc-ILD) is relatively limited. This study aimed to analyse lung HRCT images of patients wit...

Machine learning classifier is associated with mortality in interstitial lung disease: a retrospective validation study leveraging registry data.

BMC pulmonary medicine
BACKGROUND: Mortality prediction in interstitial lung disease (ILD) poses a significant challenge to clinicians due to heterogeneity across disease subtypes. Currently, forced vital capacity (FVC) and Gender, Age, and Physiology (GAP) score are the t...

Computed tomography machine learning classifier correlates with mortality in interstitial lung disease.

Respiratory investigation
BACKGROUND: A machine learning classifier system, Fibresolve, was designed and validated as an adjunct to non-invasive diagnosis in idiopathic pulmonary fibrosis (IPF). The system uses a deep learning algorithm to analyze chest computed tomography (C...