Spirometry test values can be estimated from a single chest radiograph
IntroductionPhysical measurements of expiratory flow volume and speed can be obtained using spirometry. These measurements have been used for the diagnosis and risk assessment of chronic obstructive pulmonary disease and play a crucial role in delivering early care. However, spirometry is not perfor...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-03-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1335958/full |
| _version_ | 1797271799589765120 |
|---|---|
| author | Akifumi Yoshida Chiharu Kai Chiharu Kai Hitoshi Futamura Kunihiko Oochi Satoshi Kondo Ikumi Sato Ikumi Sato Satoshi Kasai |
| author_facet | Akifumi Yoshida Chiharu Kai Chiharu Kai Hitoshi Futamura Kunihiko Oochi Satoshi Kondo Ikumi Sato Ikumi Sato Satoshi Kasai |
| author_sort | Akifumi Yoshida |
| collection | DOAJ |
| description | IntroductionPhysical measurements of expiratory flow volume and speed can be obtained using spirometry. These measurements have been used for the diagnosis and risk assessment of chronic obstructive pulmonary disease and play a crucial role in delivering early care. However, spirometry is not performed frequently in routine clinical practice, thereby hindering the early detection of pulmonary function impairment. Chest radiographs (CXRs), though acquired frequently, are not used to measure pulmonary functional information. This study aimed to evaluate whether spirometry parameters can be estimated accurately from single frontal CXR without image findings using deep learning.MethodsForced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and FEV1/FVC as spirometry measurements as well as the corresponding chest radiographs of 11,837 participants were used in this study. The data were randomly allocated to the training, validation, and evaluation datasets at an 8:1:1 ratio. A deep learning network was pretrained using ImageNet. The input and output information were CXRs and spirometry test values, respectively. The training and evaluation of the deep learning network were performed separately for each parameter. The mean absolute error rate (MAPE) and Pearson’s correlation coefficient (r) were used as the evaluation indices.ResultsThe MAPEs between the spirometry measurements and AI estimates for FVC, FEV1 and FEV1/FVC were 7.59% (r = 0.910), 9.06% (r = 0.879) and 5.21% (r = 0.522), respectively. A strong positive correlation was observed between the measured and predicted indices of FVC and FEV1. The average accuracy of >90% was obtained in each estimation of spirometry indices. Bland–Altman analysis revealed good agreement between the estimated and measured values for FVC and FEV1.DiscussionFrontal CXRs contain information related to pulmonary function, and AI estimation performed using frontal CXRs without image findings could accurately estimate spirometry values. The network proposed for estimating pulmonary function in this study could serve as a recommendation for performing spirometry or as an alternative method, suggesting its utility. |
| first_indexed | 2024-03-07T14:14:27Z |
| format | Article |
| id | doaj.art-43db051f2282489aa573cedbce55b648 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-03-07T14:14:27Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-43db051f2282489aa573cedbce55b6482024-03-06T14:01:08ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2024-03-011110.3389/fmed.2024.13359581335958Spirometry test values can be estimated from a single chest radiographAkifumi Yoshida0Chiharu Kai1Chiharu Kai2Hitoshi Futamura3Kunihiko Oochi4Satoshi Kondo5Ikumi Sato6Ikumi Sato7Satoshi Kasai8Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, JapanDepartment of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, JapanMajor in Health and Welfare, Graduate School of Niigata University of Health and Welfare, Niigata, JapanKonica Minolta, Inc., Tokyo, JapanKyoto Industrial Health Association, Kyoto, JapanGraduate School of Engineering, Muroran Institute of Technology, Muroran, JapanMajor in Health and Welfare, Graduate School of Niigata University of Health and Welfare, Niigata, JapanDepartment of Nursing, Faculty of Nursing, Niigata University of Health and Welfare, Niigata, JapanDepartment of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, JapanIntroductionPhysical measurements of expiratory flow volume and speed can be obtained using spirometry. These measurements have been used for the diagnosis and risk assessment of chronic obstructive pulmonary disease and play a crucial role in delivering early care. However, spirometry is not performed frequently in routine clinical practice, thereby hindering the early detection of pulmonary function impairment. Chest radiographs (CXRs), though acquired frequently, are not used to measure pulmonary functional information. This study aimed to evaluate whether spirometry parameters can be estimated accurately from single frontal CXR without image findings using deep learning.MethodsForced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and FEV1/FVC as spirometry measurements as well as the corresponding chest radiographs of 11,837 participants were used in this study. The data were randomly allocated to the training, validation, and evaluation datasets at an 8:1:1 ratio. A deep learning network was pretrained using ImageNet. The input and output information were CXRs and spirometry test values, respectively. The training and evaluation of the deep learning network were performed separately for each parameter. The mean absolute error rate (MAPE) and Pearson’s correlation coefficient (r) were used as the evaluation indices.ResultsThe MAPEs between the spirometry measurements and AI estimates for FVC, FEV1 and FEV1/FVC were 7.59% (r = 0.910), 9.06% (r = 0.879) and 5.21% (r = 0.522), respectively. A strong positive correlation was observed between the measured and predicted indices of FVC and FEV1. The average accuracy of >90% was obtained in each estimation of spirometry indices. Bland–Altman analysis revealed good agreement between the estimated and measured values for FVC and FEV1.DiscussionFrontal CXRs contain information related to pulmonary function, and AI estimation performed using frontal CXRs without image findings could accurately estimate spirometry values. The network proposed for estimating pulmonary function in this study could serve as a recommendation for performing spirometry or as an alternative method, suggesting its utility.https://www.frontiersin.org/articles/10.3389/fmed.2024.1335958/fullpulmonary function testchest radiographyartificial intelligencespirometrydeep learning |
| spellingShingle | Akifumi Yoshida Chiharu Kai Chiharu Kai Hitoshi Futamura Kunihiko Oochi Satoshi Kondo Ikumi Sato Ikumi Sato Satoshi Kasai Spirometry test values can be estimated from a single chest radiograph Frontiers in Medicine pulmonary function test chest radiography artificial intelligence spirometry deep learning |
| title | Spirometry test values can be estimated from a single chest radiograph |
| title_full | Spirometry test values can be estimated from a single chest radiograph |
| title_fullStr | Spirometry test values can be estimated from a single chest radiograph |
| title_full_unstemmed | Spirometry test values can be estimated from a single chest radiograph |
| title_short | Spirometry test values can be estimated from a single chest radiograph |
| title_sort | spirometry test values can be estimated from a single chest radiograph |
| topic | pulmonary function test chest radiography artificial intelligence spirometry deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1335958/full |
| work_keys_str_mv | AT akifumiyoshida spirometrytestvaluescanbeestimatedfromasinglechestradiograph AT chiharukai spirometrytestvaluescanbeestimatedfromasinglechestradiograph AT chiharukai spirometrytestvaluescanbeestimatedfromasinglechestradiograph AT hitoshifutamura spirometrytestvaluescanbeestimatedfromasinglechestradiograph AT kunihikooochi spirometrytestvaluescanbeestimatedfromasinglechestradiograph AT satoshikondo spirometrytestvaluescanbeestimatedfromasinglechestradiograph AT ikumisato spirometrytestvaluescanbeestimatedfromasinglechestradiograph AT ikumisato spirometrytestvaluescanbeestimatedfromasinglechestradiograph AT satoshikasai spirometrytestvaluescanbeestimatedfromasinglechestradiograph |