AIMC Topic: Asthma
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Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.
BMJ open
Aug 30, 2024
INTRODUCTION: Development of asthma and allergies in childhood/adolescence commonly follows a sequential progression termed the 'atopic march'. Recent reports indicate, however, that these diseases are composed of multiple distinct phenotypes, with p...
Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.
Journal of medical Internet research
Aug 23, 2024
BACKGROUND: The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even imp...
Improving Xenon-129 lung ventilation image SNR with deep-learning based image reconstruction.
Magnetic resonance in medicine
Aug 18, 2024
PURPOSE: To evaluate the feasibility and utility of a deep learning (DL)-based reconstruction for improving the SNR of hyperpolarized Xe lung ventilation MRI.
Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning.
Pediatric pulmonology
Jul 29, 2024
RATIONALE: More targeted management of severe acute pediatric asthma could improve clinical outcomes.
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...
Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study.
JMIR research protocols
Jul 8, 2024
BACKGROUND: Pediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient rep...
Concepts for the Development of Person-Centered, Digitally Enabled, Artificial Intelligence-Assisted ARIA Care Pathways (ARIA 2024).
The journal of allergy and clinical immunology. In practice
Jul 4, 2024
The traditional healthcare model is focused on diseases (medicine and natural science) and does not acknowledge patients' resources and abilities to be experts in their own lives based on their lived experiences. Improving healthcare safety, quality,...
ConvLSNet: A lightweight architecture based on ConvLSTM model for the classification of pulmonary conditions using multichannel lung sound recordings.
Artificial intelligence in medicine
Jun 22, 2024
Characterization of lung sounds (LS) is indispensable for diagnosing respiratory pathology. Although conventional neural networks (NNs) have been widely employed for the automatic diagnosis of lung sounds, deep neural networks can potentially be more...
DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.
BMJ open respiratory research
May 22, 2024
INTRODUCTION: Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are ...
Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.
Journal of medical systems
May 13, 2024
Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in...
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