AIMC Topic: Bronchiectasis

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Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.

Pediatric pulmonology Jul 23, 2024
OBJECTIVES: Chest high-resolution computed tomography (HRCT) is conditionally recommended to rule out conditions that mimic or coexist with severe asthma in children. However, it may provide valuable insights into identifying structural airway change...
Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Case-Control Studies Adolescent Child Bronchiectasis Female Asthma Male Severity of Illness Index
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Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning.

Frontiers in cellular and infection microbiology May 23, 2024
BACKGROUND: Lower respiratory tract infections represent prevalent ailments. Nonetheless, current comprehension of the microbial ecosystems within the lower respiratory tract remains incomplete and necessitates further comprehensive assessment. Lever...
Respiratory Tract Infections Male Aged Bronchoalveolar Lavage Fluid Middle Aged Bacteria Bronchiectasis Humans Lung Metagenome Aged, 80 and over Machine Learning Adolescent Microbiota Young Adult Female Adult High-Throughput Nucleotide Sequencing Hospitalization Metagenomics
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Artificial Intelligence-based CT Assessment of Bronchiectasis: The COPDGene Study.

Radiology Dec 13, 2022
Background CT is the standard method used to assess bronchiectasis. A higher airway-to-artery diameter ratio (AAR) is typically used to identify enlarged bronchi and bronchiectasis; however, current imaging methods are limited in assessing the extent...
Female Aged Pulmonary Disease, Chronic Obstructive Male Humans Artificial Intelligence Smokers Bronchiectasis Bronchi Tomography, Emission-Computed Middle Aged Cohort Studies Follow-Up Studies Regression Analysis
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Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images.

BioMed research international Sep 29, 2020
An increasing number of patients infected with nontuberculous mycobacteria (NTM) are observed worldwide. However, it is challenging to identify NTM lung diseases from pulmonary tuberculosis (PTB) due to considerable overlap in classic manifestations ...
Bronchiectasis Nontuberculous Mycobacteria Adult Sensitivity and Specificity Male Middle Aged Lung Tuberculosis, Pulmonary Mycobacterium Infections, Nontuberculous Machine Learning Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Female Aged Humans
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Fast and accurate reconstruction of human lung gas MRI with deep learning.

Magnetic resonance in medicine Jul 19, 2019
PURPOSE: To fast and accurately reconstruct human lung gas MRI from highly undersampled k-space using deep learning.
Retrospective Studies Female Reproducibility of Results Aged Young Adult Adult Protons Pulmonary Disease, Chronic Obstructive Asthma Xenon Isotopes Humans Algorithms Prospective Studies Solitary Pulmonary Nodule Tuberculosis, Pulmonary Respiration Male Imaging, Three-Dimensional Healthy Volunteers Signal-To-Noise Ratio Middle Aged Smoking Image Processing, Computer-Assisted Bronchiectasis Lung Inflammation Fourier Analysis Deep Learning Magnetic Resonance Imaging
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Surgery for predominant lesion in nonlocalized bronchiectasis.

The Journal of thoracic and cardiovascular surgery Dec 15, 2016
OBJECTIVE: Patients with nonlocalized bronchiectasis are encountered commonly; however, there is little information regarding surgical intervention in this patient population. The aim of this study was to evaluate symptomatic response and safety of a...
Postoperative Complications Risk Factors Humans Pneumonectomy Tomography, X-Ray Computed Male Time Factors Thoracotomy Female Middle Aged Bronchiectasis Treatment Outcome Risk Assessment Bronchoscopy
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Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease Mar 1, 2016
BACKGROUND: Unsupervised learning technique allows researchers to identify different phenotypes of diseases with complex manifestations.
Bronchiectasis Risk Factors Sputum Aged Follow-Up Studies Forced Expiratory Volume Humans Adult Anti-Bacterial Agents Middle Aged Cluster Analysis Male Phenotype Quality of Life Pseudomonas aeruginosa Unsupervised Machine Learning Prospective Studies Cohort Studies Cough Female Body Mass Index
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