AIMC Topic: Lung Diseases

Clear Filters Showing 151 to 160 of 179 articles

Artificial Intelligence Analysis of Chest Radiographs for Predicting Major Adverse Events in Patients Visiting the Emergency Department With Acute Cardiopulmonary Symptoms.

Korean journal of radiology
OBJECTIVE: In this study, we investigated whether artificial intelligence (AI) analysis of chest radiographs (CXRs) can predict major adverse clinical events in patients visiting the emergency department (ED) with acute cardiopulmonary symptoms.

Content-based X-ray image retrieval using fusion of local neighboring patterns and deep features for lung disease detection.

Radiological physics and technology
This paper introduces a Content-Based Medical Image Retrieval (CBMIR) system for detecting and retrieving lung disease cases to assist doctors and radiologists in clinical decision-making. The system combines texture-based features using Local Binary...

A review: Lightweight architecture model in deep learning approach for lung disease identification.

Computers in biology and medicine
As one of the leading causes of death worldwide, early detection of lung disease is a very important step to improve the effectiveness of treatment. By using medical image data, such as X-ray or CT-scan, classification of lung disease can be done. De...

[Application and progress of artificial intelligence technology in interventional pulmonology].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases
In recent years, interventional pulmonology has advanced rapidly, with bronchoscopy becoming a cornerstone in the diagnosis and treatment of respiratory diseases. The integration of artificial intelligence (AI) technology has established an intellige...

Non-invasive diagnosis of lung diseases via multimodal feature extraction from breathing audio and chest dynamics.

Computers in biology and medicine
Early and accurate diagnosis of lung diseases is crucial for effective treatment. While traditional methods have limitations, audio analysis offers a promising non-invasive approach. However, existing studies often rely solely on acoustic features, n...

Transforming pulmonary health care: the role of artificial intelligence in diagnosis and treatment.

Expert review of respiratory medicine
INTRODUCTION: Respiratory diseases like pneumonia, asthma, and COPD are major global health concerns, significantly impacting morbidity and mortality rates worldwide.

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

Studies in health technology and informatics
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 en...

Artificial intelligence in bronchoscopy: a systematic review.

European respiratory review : an official journal of the European Respiratory Society
BACKGROUND: Artificial intelligence (AI) systems have been implemented to improve the diagnostic yield and operators' skills within endoscopy. Similar AI systems are now emerging in bronchoscopy. Our objective was to identify and describe AI systems ...

Machine learning-based risk assessment for cardiovascular diseases in patients with chronic lung diseases.

Medicine
The association between chronic lung diseases (CLDs) and the risk of cardiovascular diseases (CVDs) has been extensively recognized. Nevertheless, conventional approaches for CVD risk evaluation cannot fully capture the risk factors (RFs) related to ...

Bridging the Gap in Neonatal Care: Evaluating AI Chatbots for Chronic Neonatal Lung Disease and Home Oxygen Therapy Management.

Pediatric pulmonology
OBJECTIVE: To evaluate the accuracy and comprehensiveness of eight free, publicly available large language model (LLM) chatbots in addressing common questions related to chronic neonatal lung disease (CNLD) and home oxygen therapy (HOT).