Cargando…
Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD
INTRODUCTION: Because of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811121/ https://www.ncbi.nlm.nih.gov/pubmed/36619622 http://dx.doi.org/10.3389/fmed.2022.980950 |
_version_ | 1784863463105888256 |
---|---|
author | Yang, Yingjian Chen, Ziran Li, Wei Zeng, Nanrong Guo, Yingwei Wang, Shicong Duan, Wenxin Liu, Yang Chen, Huai Li, Xian Chen, Rongchang Kang, Yan |
author_facet | Yang, Yingjian Chen, Ziran Li, Wei Zeng, Nanrong Guo, Yingwei Wang, Shicong Duan, Wenxin Liu, Yang Chen, Huai Li, Xian Chen, Rongchang Kang, Yan |
author_sort | Yang, Yingjian |
collection | PubMed |
description | INTRODUCTION: Because of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier. METHODS: First, lung region images are automatically segmented from chest HRCT images for extracting the original 1,316 lung radiomics (OLR, 1,316) and 13,824 3D CNN features (O3C, 13,824). Second, the local features, including five selected pulmonary function test (PFT) parameters (SLF, 5), 28 selected lung radiomics (SLR, 28), and 22 selected 3D CNN features (S3C, 22), are respectively selected from the original 11 PFT parameters (OLF, 11), 1,316 OLR, and 13,824 O3C by the least absolute shrinkage and selection operator (Lasso) algorithm. Meantime, the global features, including two fused PFT parameters (FLF, 2), six fused lung radiomics (FLR, 6), and 34 fused 3D CNN features (F3C, 34), are respectively fused by 11 OLF, 1,316 OLR, and 13,824 O3C using the principal component analysis (PCA) algorithm. Finally, we combine all the local and global features (SLF + FLF + SLR + FLR + S3C + F3C, 5+ 2 + 28 + 6 + 22 + 34) for dyspnea identification in COPD based on the MLP classifier. RESULTS: Our proposed method comprehensively improves classification performance. The MLP classifier with all the local and global features achieves the best classification performance at 87.7% of accuracy, 87.7% of precision, 87.7% of recall, 87.7% of F1-scorel, and 89.3% of AUC, respectively. DISCUSSION: Compared with single-modal data, the proposed strategy effectively improves the classification performance for dyspnea identification in COPD, providing an objective and effective tool for COPD management. |
format | Online Article Text |
id | pubmed-9811121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98111212023-01-05 Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD Yang, Yingjian Chen, Ziran Li, Wei Zeng, Nanrong Guo, Yingwei Wang, Shicong Duan, Wenxin Liu, Yang Chen, Huai Li, Xian Chen, Rongchang Kang, Yan Front Med (Lausanne) Medicine INTRODUCTION: Because of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier. METHODS: First, lung region images are automatically segmented from chest HRCT images for extracting the original 1,316 lung radiomics (OLR, 1,316) and 13,824 3D CNN features (O3C, 13,824). Second, the local features, including five selected pulmonary function test (PFT) parameters (SLF, 5), 28 selected lung radiomics (SLR, 28), and 22 selected 3D CNN features (S3C, 22), are respectively selected from the original 11 PFT parameters (OLF, 11), 1,316 OLR, and 13,824 O3C by the least absolute shrinkage and selection operator (Lasso) algorithm. Meantime, the global features, including two fused PFT parameters (FLF, 2), six fused lung radiomics (FLR, 6), and 34 fused 3D CNN features (F3C, 34), are respectively fused by 11 OLF, 1,316 OLR, and 13,824 O3C using the principal component analysis (PCA) algorithm. Finally, we combine all the local and global features (SLF + FLF + SLR + FLR + S3C + F3C, 5+ 2 + 28 + 6 + 22 + 34) for dyspnea identification in COPD based on the MLP classifier. RESULTS: Our proposed method comprehensively improves classification performance. The MLP classifier with all the local and global features achieves the best classification performance at 87.7% of accuracy, 87.7% of precision, 87.7% of recall, 87.7% of F1-scorel, and 89.3% of AUC, respectively. DISCUSSION: Compared with single-modal data, the proposed strategy effectively improves the classification performance for dyspnea identification in COPD, providing an objective and effective tool for COPD management. Frontiers Media S.A. 2022-12-21 /pmc/articles/PMC9811121/ /pubmed/36619622 http://dx.doi.org/10.3389/fmed.2022.980950 Text en Copyright © 2022 Yang, Chen, Li, Zeng, Guo, Wang, Duan, Liu, Chen, Li, Chen and Kang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Yang, Yingjian Chen, Ziran Li, Wei Zeng, Nanrong Guo, Yingwei Wang, Shicong Duan, Wenxin Liu, Yang Chen, Huai Li, Xian Chen, Rongchang Kang, Yan Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD |
title | Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD |
title_full | Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD |
title_fullStr | Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD |
title_full_unstemmed | Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD |
title_short | Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD |
title_sort | multi-modal data combination strategy based on chest hrct images and pft parameters for intelligent dyspnea identification in copd |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811121/ https://www.ncbi.nlm.nih.gov/pubmed/36619622 http://dx.doi.org/10.3389/fmed.2022.980950 |
work_keys_str_mv | AT yangyingjian multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT chenziran multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT liwei multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT zengnanrong multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT guoyingwei multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT wangshicong multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT duanwenxin multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT liuyang multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT chenhuai multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT lixian multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT chenrongchang multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd AT kangyan multimodaldatacombinationstrategybasedonchesthrctimagesandpftparametersforintelligentdyspneaidentificationincopd |