PULMONARY FUNCTION TESTING AND AI

2024_12

Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep Learning

Lee et al.

https://pubs.rsna.org/doi/10.1148/ryct.240005

Highlights

This papers used data from 8893 patients in the COPDGene Phase 1 cohort. The authors attempted to use a CNN (convolutional neural network) to estimate spirometric data from CT scans. The spirometric stage predicted by the CNN, using CT scans, showed moderate to good agreement with the reference (standard spirometric staging). The prediction improved with providing clinical data.

2024_10

CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression

Saha et al

https://pubmed.ncbi.nlm.nih.gov/39404745/

Highlights

This paper evaluated 4575 subjects based on CT scans and air flow limitations. The authors differentiated rates of decline in lung denities using 4 models - base clinical model, CT density, radiomics and combined features. The radiomics-only model and combined model both outperformed the base clinical model in detecting rapid progressors.

2024_11

Radiomultiomics: quantitative CT clusters of severe asthma associated with multiomics

Kermani et al.

https://publications.ersnet.org/content/erj/64/5/2400207

Highlights

The paper takes a multi-omic approach and incorporates radiologic, pathologic (biopsy, brushing, sputum) and serologic (blood) information to identify asthma clusters.

2023_11

Home Monitoring of Asthma Exacerbations in Children and Adults With Use of an AI-Aided Stethoscope

Emeryk et al.

https://pmc.ncbi.nlm.nih.gov/articles/PMC10681685/

Highlights

The AI-aided home stethoscope provides reliable information on asthma exacerbations. The parameters provided are effective for children, especially those younger than 5 years of age. The introduction of this tool to the health care system might enhance asthma exacerbation detection substantially and make remote monitoring of patients easier.