Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes
| Title: | Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes |
|---|---|
| Authors: | Gerlig Widmann; Anna Katharina Luger; Thomas Sonnweber; Christoph Schwabl; Katharina Cima; Anna Katharina Gerstner; Alex Pizzini; Sabina Sahanic; Anna Boehm; Maxmilian Coen; Ewald Wöll; Günter Weiss; Rudolf Kirchmair; Leonhard Gruber; Gudrun M. Feuchtner; Ivan Tancevski; Judith Löffler-Ragg; Piotr Tymoszuk |
| Source: | Diagnostics ; Volume 15 ; Issue 6 ; Pages: 783 |
| Publisher Information: | Multidisciplinary Digital Publishing Institute |
| Publication Year: | 2025 |
| Collection: | MDPI Open Access Publishing |
| Subject Terms: | artificial intelligence; lung CT; quantification; COVID-19 |
| Description: | Objectives: Prediction of lung function deficits following pulmonary infection is challenging and suffers from inaccuracy. We sought to develop machine-learning models for prediction of post-inflammatory lung changes based on COVID-19 recovery data. Methods: In the prospective CovILD study (n = 420 longitudinal observations from n = 140 COVID-19 survivors), data on lung function testing (LFT), chest CT including severity scoring by a human radiologist and density measurement by artificial intelligence, demography, and persistent symptoms were collected. This information was used to develop models of numeric readouts and abnormalities of LFT with four machine learning algorithms (Random Forest, gradient boosted machines, neural network, and support vector machines). Results: Reduced DLCO (diffusion capacity for carbon monoxide |
| Document Type: | text |
| File Description: | application/pdf |
| Language: | English |
| Relation: | Medical Imaging and Theranostics; https://dx.doi.org/10.3390/diagnostics15060783 |
| DOI: | 10.3390/diagnostics15060783 |
| Availability: | https://doi.org/10.3390/diagnostics15060783 |
| Rights: | https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: | edsbas.70C94006 |
| Database: | BASE |