AIMC Topic: Pulmonary Disease, Chronic Obstructive

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Machine learning-assisted construction of COPD self-evaluation questionnaire (COPD-EQ): a national multicentre study in China.

Journal of global health
BACKGROUND: Approximately 70% of chronic obstructive pulmonary disease (COPD) is underdiagnosed worldwide. We aimed to develop and validate a COPD self-evaluation questionnaire (COPD-EQ) that is better suited for COPD screening in China.

A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system.

PloS one
BACKGROUND: Dyspnoea is one of the emergency department's (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart...

Optimizing convolutional neural networks for Chronic Obstructive Pulmonary Disease detection in clinical computed tomography imaging.

Computers in biology and medicine
We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO)...

The Pivotal Role of Baseline LDCT for Lung Cancer Screening in the Era of Artificial Intelligence.

Archivos de bronconeumologia
In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and explore the contributions of artificial intelligence (AI), in overcoming them. We focus on evaluating th...

BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, du...

Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.

JCI insight
Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high di...

Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms.

International journal of chronic obstructive pulmonary disease
BACKGROUND AND PURPOSE: Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors, and genetic factors are important determinants of COPD. This study focuses on scree...

Identification of Nocturnal Leg Cramps and Affecting Factors in COPD Patients: Logistic Regression and Artificial Neural Network.

Clinical nursing research
Although there are many sleep-related complaints in chronic obstructive pulmonary disease (COPD) patients, nocturnal leg cramps have not been adequately and extensively studied. This study fills a significant gap in the literature by determining the ...

Advancements in automated classification of chronic obstructive pulmonary disease based on computed tomography imaging features through deep learning approaches.

Respiratory medicine
Chronic Obstructive Pulmonary Disease (COPD) represents a global public health issue that significantly impairs patients' quality of life and overall health. As one of the primary causes of chronic respiratory diseases and global mortality, effective...