Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation
Overall, the models demonstrate robust performance across lung volume measurements, underscoring their potential utility in clinical practice for accurate diagnosis and prognosis of respiratory conditions, particularly in settings where access to body plethysmography or other lung volume measurement modalities is limited.
Authors
- Scott A. Helgeson (ORCID: https://orcid.org/0000-0001-7590-2293)
- Rickey E. Carter (ORCID: https://orcid.org/0000-0002-0818-273X)
- Patrick W. Johnson (ORCID: https://orcid.org/0000-0001-8365-1375)
- Kaiser G. Lim (ORCID: https://orcid.org/0000-0002-4551-6559)
- Augustine S. Lee (ORCID: https://orcid.org/0000-0001-8018-5145)
- Zachary Quicksall (ORCID: https://orcid.org/0000-0002-8791-0925)
Institutions
- Mayo Clinic (US)
- Jacksonville College (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-03-24
- DOI
- https://doi.org/10.2196/65456
- Citations
- 2
- Primary Topic
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Type
- article
- Field-Weighted Citation Impact
- 3.04