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      Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD

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          Abstract

          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.

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          Random Forests

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            Regression Shrinkage and Selection Via the Lasso

            Robert Tibshirani (1996)
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              Greedy function approximation: A gradient boosting machine.

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                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Med (Lausanne)
                Journal ID (iso-abbrev): Front Med (Lausanne)
                Journal ID (publisher-id): Front. Med.
                Title: Frontiers in Medicine
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 2296-858X
                Publication date (Electronic): 21 December 2022
                Publication date Collection: 2022
                Volume: 9
                Electronic Location Identifier: 980950
                Affiliations
                [1] 1College of Medicine and Biological Information Engineering, Northeastern University , Shenyang, China
                [2] 2College of Health Science and Environmental Engineering, Shenzhen Technology University , Shenzhen, China
                [3] 3School of Applied Technology, Shenzhen University , Shenzhen, China
                [4] 4Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University , Guangzhou, China
                [5] 5Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital , Shenzhen, China
                [6] 6The Second Clinical Medical College, Jinan University , Guangzhou, China
                [7] 7The First Affiliated Hospital, Southern University of Science and Technology , Shenzhen, China
                [8] 8Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education , Shenyang, China
                Author notes

                Edited by: Hyunjin Park, Sungkyunkwan University, South Korea

                Reviewed by: Christophe Delclaux, Hôpital Robert Debré, France; Yukun Dong, China University of Petroleum, Huadong, China; Hui Zhou, Nanjing University of Science and Technology, China

                *Correspondence: Yan Kang ✉ kangyan@ 123456sztu.edu.cn
                Rongchang Chen ✉ chenrc@ 123456vip.163.com

                This article was submitted to Precision Medicine, a section of the journal Frontiers in Medicine

                †These authors have contributed equally to this work and share first authorship

                Article
                DOI: 10.3389/fmed.2022.980950
                PMC ID: 9811121
                SO-VID: 9437d5cb-4bac-420e-a907-00a12f8a13ce
                Copyright © Copyright © 2022 Yang, Chen, Li, Zeng, Guo, Wang, Duan, Liu, Chen, Li, Chen and Kang.
                License:

                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.

                History
                Date received : 30 June 2022
                Date accepted : 06 December 2022
                Page count
                Figures: 8, Tables: 7, Equations: 7, References: 55, Pages: 21, Words: 10825
                Categories
                Subject: Medicine
                Subject: Original Research

                Keywords: dyspnea identification,copd,multi-modal data,combination strategy,pft parameters,lung radiomics features,3d cnn features,machine learning
                Data availability:
                Keywords: dyspnea identification, copd, multi-modal data, combination strategy, pft parameters, lung radiomics features, 3d cnn features, machine learning

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