Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images.

Background: Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability.

Objective: To solve these problems, in this work, we propose a deep learning driven framework that can assess and quantify lesion indicators and outcome the prediction of severity of ILD.

Methods: In detail, we first present a convolutional neural network that can segment and quantify five types of lesions including HC, RO, GGO, CONS, and EMPH from HRCT of ILD patients, and then we conduct quantitative analysis to select the features related to ILD based on the segmented lesions and clinical data.

Computed Tomography-Based Deep Learning Model for Assessing the Severity of Patients With Connective Tissue Disease-Associated Interstitial Lung Disease.

Objectives: This study aimed to develop a computed tomography (CT)-based deep learning model for assessing the severity of patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD).

Methods: The retrospective study included 298 CTD-ILD patients between January 2018 and May 2022. A deep learning-based RDNet model was established (1610 fully annotated CT images for training and 402 images for validation).

Can CT-Based Arterial and Venous Morphological Markers of Chronic Obstructive Pulmonary Disease Explain Pulmonary Vascular Remodeling?

Rationale And Objectives: We analyzed changes in quantitative pulmonary artery and vein parameters to investigate pulmonary vascular remodeling characteristics in chronic obstructive pulmonary disease (COPD) patients.

Materials And Methods: This retrospective study recruited healthy volunteers and COPD patients. Participants undergoing standard-of-care pulmonary function testing (PFT) and computed tomography (CT) evaluations were classified into five groups: normal and Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1-4.

Computed Tomography Radiomics-based Prediction Model for Gender-Age-Physiology Staging of Connective Tissue Disease-associated Interstitial Lung Disease.

Purpose: To analyze the feasibility of predicting gender-age-physiology (GAP) staging in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD) by radiomics based on computed tomography (CT) of the chest.

Materials And Methods: Chest CT images of 184 patients with CTD-ILD were retrospectively analyzed. GAP staging was performed on the basis of gender, age, and pulmonary function test results.