AIMC Topic: Respiratory Function Tests

Clear Filters Showing 21 to 30 of 66 articles

Pattern recognition of forced oscillation technique measurement results using deep learning can identify asthmatic patients more accurately than setting reference ranges.

Scientific reports
No official clinical reference values have been established for MostGraph, which measures total respiratory resistance and reactance using the forced oscillation technique, complicating result interpretation. This study aimed to establish a reference...

Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation.

The European respiratory journal
BACKGROUND: Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (e...

[Quantitative Evaluation of Airway Lesions in Chronic Obstructive Pulmonary Disease by Applying Deep Learning Reconstruction to Ultra-high-resolution CT Images: Correlation between Wall Area Percentage and Forced Expiratory Volume in One Second Percentage].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: Using ultra-high-resolution images reconstructed with the Advanced intelligent Clear-IQ Engine (AiCE) lung to measure wall area percentage (WA%), we demonstrated that WA% measured in more distal bronchus has a stronger correlation with respi...

Deep Learning Prediction of Survival in Patients with Chronic Obstructive Pulmonary Disease Using Chest Radiographs.

Radiology
Background Preexisting indexes for predicting the prognosis of chronic obstructive pulmonary disease (COPD) do not use radiologic information and are impractical because they involve complex history assessments or exercise tests. Purpose To develop a...

Deep learning for spirometry quality assurance with spirometric indices and curves.

Respiratory research
BACKGROUND: Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitiv...

Early heart rate variability evaluation enables to predict ICU patients' outcome.

Scientific reports
Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has been proposed in various types of patients. We attempted to evaluate the best determinants of such var...

Artificial intelligence for quality control of oscillometry measures.

Computers in biology and medicine
BACKGROUND: The forced oscillation technique (FOT) allows non-invasive lung function testing during quiet breathing even without expert guidance. However, it still relies on an operator for excluding breaths with artefacts such as swallowing, glottis...

Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease.

Scientific reports
Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of CO...