AIMC Topic: Asthma
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Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening.
Respiratory research
Nov 28, 2023
OBJECTIVES: Parametric response mapping (PRM) enables the evaluation of small airway disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT scans. We hypothesize that deep learning PRM from inspiratory chest CT scans ...
How AI is advancing asthma management? Insights into economic and clinical aspects.
Journal of medical economics
Nov 11, 2023
Asthma, an increasingly prevalent chronic respiratory condition, incurs significant economic costs worldwide. Artificial Intelligence (AI), particularly Machine Learning (ML), has been widely recognized as transformative when applied to asthma care. ...
Untargeted metabolomic profiling in children identifies novel pathways in asthma and atopy.
The Journal of allergy and clinical immunology
Oct 29, 2023
BACKGROUND: Asthma and other atopic disorders can present with varying clinical phenotypes marked by differential metabolomic manifestations and enriched biological pathways.
Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques.
Computers in biology and medicine
Sep 29, 2023
Bronchial asthma is a prevalent non-communicable disease among children. The study collected clinical data from 390 children aged 4-17 years with asthma, with or without rhinitis, who received allergen immunotherapy (AIT). Combining these data, this ...
Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method.
Scientific reports
Sep 16, 2023
Severe asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe asthma may help identify therapeutic strategies for this complex condition. RNA expression data wer...
Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation.
PloS one
Sep 5, 2023
The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow limitation severity and diffusing capacity for carbon monoxide, which are widely used in diagnosing ...
Home monitoring with connected mobile devices for asthma attack prediction with machine learning.
Scientific data
Jun 8, 2023
Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined wit...
Imaging-derived biomarkers in Asthma: Current status and future perspectives.
Respiratory medicine
Jan 23, 2023
Asthma is a common disorder affecting around 315 million individuals worldwide. The heterogeneity of asthma is becoming increasingly important in the era of personalized treatment and response assessment. Several radiological imaging modalities are a...
Deep Learning-Based Segmentation of Airway Morphology from Endobronchial Optical Coherence Tomography.
Respiration; international review of thoracic diseases
Jan 19, 2023
BACKGROUND: Manual measurement of endobronchial optical coherence tomography (EB-OCT) images means a heavy workload in the clinical practice, which can also introduce bias if the subjective opinions of doctors are involved.
Children's views on artificial intelligence and digital twins for the daily management of their asthma: a mixed-method study.
European journal of pediatrics
Dec 13, 2022
New technologies enable the creation of digital twin systems (DTS) combining continuous data collection from children's home and artificial intelligence (AI)-based recommendations to adapt their care in real time. The objective was to assess whether ...
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