COPD

Using 3D point cloud and graph-based neural networks to improve the estimation of pulmonary function tests from chest CT.

Pulmonary function tests (PFTs) are important clinical metrics to measure the severity of interstitial lung disease for systemic sclerosis patients. However, PFTs cannot always be performed by spirometry if there is a risk of disease transmission or other contraindications. In addition, it is unclear how lung function is affected by changes in lung vessels. Therefore, convolution neural networks (...

Screening COPD-Related Biomarkers and Traditional Chinese Medicine Prediction Based on Bioinformatics and Machine Learning.

PURPOSE: To employ bioinformatics and machine learning to predict the characteristics of immune cells and genes associated with the inflammatory response and ferroptosis in chronic obstructive pulmonary disease (COPD) patients and to aid in the development of targeted traditional Chinese medicine (TCM). Mendelian randomization analysis elucidates the causal relationships among immune cells, genes,...

Machine-Learning Model Identifies Patients With Alpha-1 Antitrypsin Deficiency Using Claims Records.

Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify ...

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

Although there are many sleep-related complaints in chronic obstructive pulmonary disease (COPD) patients, nocturnal leg cramps have not been adequate...

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

Chronic Obstructive Pulmonary Disease (COPD) represents a global public health issue that significantly impairs patients' quality of life and overall ...

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis.

BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of l...

Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review.

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disease and ranks third in global mortality rates, imposing a significant burd...

Improved detection of small pulmonary embolism on unenhanced computed tomography using an artificial intelligence-based algorithm - a single centre retrospective study.

To preliminarily verify the feasibility of a deep-learning (DL) artificial intelligence (AI) model to localize pulmonary embolism (PE) on unenhanced c...

Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning.

RATIONALE AND OBJECTIVES: The structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stab...

Artificial intelligence in COPD CT images: identification, staging, and quantitation.

Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations oft...

Artificial Intelligence in Chronic Obstructive Pulmonary Disease: Research Status, Trends, and Future Directions --A Bibliometric Analysis from 2009 to 2023.

OBJECTIVE: A bibliometric analysis was conducted using VOSviewer and CiteSpace to examine studies published between 2009 and 2023 on the utilization o...

COPDVD: Automated classification of chronic obstructive pulmonary disease on a new collected and evaluated voice dataset.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The ab...

Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study.

BACKGROUND: For medical artificial intelligence (AI) training and validation, human expert labels are considered the gold standard that represents the...

Identification and Validation of Aging Related Genes Signature in Chronic Obstructive Pulmonary Disease.

PURPOSE: Chronic Obstructive Pulmonary Disease (COPD) is regarded as an accelerated aging disease. Aging-related genes in COPD are still poorly unders...

Machine learning-assisted label-free colorectal cancer diagnosis using plasmonic needle-endoscopy system.

Early and accurate detection of colorectal cancer (CRC) is critical for improving patient outcomes. Existing diagnostic techniques are often invasive ...

Structure and position-aware graph neural network for airway labeling.

We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airwa...

MedExpQA: Multilingual benchmarking of Large Language Models for Medical Question Answering.

Large Language Models (LLMs) have the potential of facilitating the development of Artificial Intelligence technology to assist medical experts for in...

Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning.

INTRODUCTION: Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of...

Smoking Classification Using Novel Plasma Cytokines by implementing Machine Learning and Statistical Methods.

Smoking is a major cause of premature and preventable death. Tobacco exposure has a detrimental effect on many organs and contributes to multiple dise...

Artificial neural networks trained on simulated multispectral data for real-time imaging of skin microcirculatory blood oxygen saturation.

SIGNIFICANCE: Imaging blood oxygen saturation ( ) in the skin can be of clinical value when studying ischemic tissue. Emerging multispectral snapshot...