COPD

LizAI XT -- Artificial Intelligence-Powered Platform for Healthcare Data Management: A Study on Clinical Data Mega-Structure, Semantic Search, and Insights of Sixteen Diseases

AI-powered LizAI XT ensures real-time and accurate mega-structure of different clinical datasets and largely inaccessible and fragmented sources, into one comprehensive table or any designated forms, based on diseases, clinical variables, and/or other defined parameters. We evaluate the platform's performance on a cluster of 4x NVIDIA A30 GPU 24GB, with 16 diseases -- from deathly cancer and COP...

Artificial Intelligence Powered Audiomics: The Futuristic Biomarker in Pulmonary Medicine - A State-of-the-Art Review.

AI-driven "audiomics" leverages voice and respiratory sounds as non-invasive biomarkers to diagnose and manage pulmonary conditions, including COVID-19, tuberculosis, ILD, asthma, and COPD. By analyzing acoustic features, machine and deep learning enhance diagnostic accuracy and track disease progression. Key applications include cough-based TB detection, smartphone COVID-19 screening, and speech ...

Practical Approach for Evaluating Machine Learning Anomaly Detection Algorithms for Epidemic Early Warning Systems.

Anomaly detection methods in time series data can play a pivotal role in epidemic surveillance Early Warning Systems (EWS). Statistical and rules-base...

Deep Learning-Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common and progressive respiratory condition characterized by persistent airflow limitat...

A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS.

BACKGROUND: Depression associated with Chronic Obstructive Pulmonary Disease (COPD) is a detrimental complication that significantly impairs patients'...

AI-based prediction of flow dynamics of blood blended with gold and maghemite nanoparticles in an electromagnetic microchannel under abruptly changes in pressure gradient.

In cardiovascular research, electromagnetic fields (EMFs) induced by Riga plates are applied to study and potentially manipulate blood flow dynamics, ...

SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning.

To develop scanning electron microscopy-based Trainable Weka (Waikato Environment for Knowledge Analysis) Intelligent Segmentation Technology (SEMTWI...

AI-Enabled Accurate Non-Invasive Assessment of Pulmonary Hypertension Progression via Multi-Modal Echocardiography

Echocardiographers can detect pulmonary hypertension using Doppler echocardiography; however, accurately assessing its progression often proves chal...

Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors.

PURPOSE: By using data obtained with digital inhalers, machine learning models have the potential to detect early signs of deterioration and predict i...

A Call for Integrated Approaches in Digital Technology Design for Aging and Disability.

The fields of aging and disability often proceed as 2 distinct lines of inquiry and action in terms of digital technology design. Guidelines and stand...

The Accuracy of ChatGPT-4o in Interpreting Chest and Abdominal X-Ray Images.

Large language models (LLMs), such as ChatGPT, have emerged as potential clinical support tools to enhance precision in personalized patient care, bu...

Metabolomic Plasma Profile of Chronic Obstructive Pulmonary Disease Patients.

The analysis of blood metabolites may help identify individuals at risk of having COPD and offer insights into its underlying pathophysiology. This st...

Identifying GAP43, NMU, and TEX29 as Potential Prognostic Biomarkers for COPD Combined With Lung Cancer Patients Using Machine Learning.

Chronic obstructive pulmonary disease (COPD) and lung cancer, frequently comorbid conditions intricately linked through smoking, represent significant...

Deep Learning for Predicting Acute Exacerbation and Mortality of Interstitial Lung Disease.

Some patients with interstitial lung disease (ILD) have a high mortality rate or experience acute exacerbation of ILD (AE-ILD) that results in increa...

Clinical and Social Characterization of Patients Hospitalized for COPD Exacerbation Using Machine Learning Tools.

OBJECTIVE: This study aims to employ machine learning (ML) tools to cluster patients hospitalized for acute exacerbations of chronic obstructive pulmo...

Predicting outcomes after hospitalisation for COPD exacerbation using machine learning.

BACKGROUND: Early readmission and death are critical adverse outcomes following hospitalisation due to exacerbation of chronic obstructive pulmonary d...

Effects of chronic exposure to biomass pollutants on cardiorespiratory responses and the occurrence of exercise-induced bronchoconstriction in healthy men.

Exposure to charcoal biomass (CB) pollutants affects the cardiorespiratory system. We assessed cardiopulmonary responses (CPR) to exercise in charcoal...

Severity Classification of Chronic Obstructive Pulmonary Disease in Intensive Care Units: A Semi-Supervised Approach Using MIMIC-III Dataset

Chronic obstructive pulmonary disease (COPD) represents a significant global health burden, where precise severity assessment is particularly critic...

Respiratory Inhaler Sound Event Classification Using Self-Supervised Learning

Asthma is a chronic respiratory condition that affects millions of people worldwide. While this condition can be managed by administering controller...

Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.

Advances in wearable sensors and artificial intelligence have greatly enhanced the potential of digitised audio biomarkers for disease diagnostics and...