Deep learning based CT images for lung function prediction in patients with chronic obstructive pulmonary disease

The preliminary research results indicate that the MLP-based multimodal feature fusion model has the potential to predict PFT parameters in COPD patients in real time. However, it is worth noting that the study used indicators before the use of bronchodilators, which may affect the interpretation of the results. Future studies should use measurements taken after bronchodilator administration to better align with clinical standards.

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Publication Details

Journal
BMC Pulmonary Medicine
Published
2025-10-21
DOI
https://doi.org/10.1186/s12890-025-03957-7
Citations
1
Primary Topic
Chronic Obstructive Pulmonary Disease (COPD) Research
Type
article
Field-Weighted Citation Impact
1.52
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article

Deep learning based CT images for lung function prediction in patients with chronic obstructive pulmonary disease

Ruihan Li, Junjie Han, Shuqin Kang, Qian Wu et al.
1 citations
BMC Pulmonary Medicine
Chronic Obstructive Pulmonary Disease (COPD) Research
1.52
article

Deep learning based CT images for lung function prediction in patients with chronic obstructive pulmonary disease

Ruihan Li, Junjie Han, Shuqin Kang, Qian Wu, Hui Guo
article en
1 citations

Abstract

The preliminary research results indicate that the MLP-based multimodal feature fusion model has the potential to predict PFT parameters in COPD patients in real time. However, it is worth noting that the study used indicators before the use of bronchodilators, which may affect the interpretation of the results. Future studies should use measurements taken after bronchodilator administration to better align with clinical standards.

BMC Pulmonary MedicineVol. 25(1)
Xinjiang Medical University (CN)
Openalex Percentile: Top 13%
Chronic Obstructive Pulmonary Disease (COPD) Research
1.52
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