Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort study
Abstract Introduction A large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral b...
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SpringerOpen
2025-01-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-024-01817-2 |
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| author | Chengbin Huang Dengying Wu Bingzhang Wang Chenxuan Hong Jiasen Hu Zijian Yan Jianpeng Chen Yaping Jin Yingze Zhang |
| author_facet | Chengbin Huang Dengying Wu Bingzhang Wang Chenxuan Hong Jiasen Hu Zijian Yan Jianpeng Chen Yaping Jin Yingze Zhang |
| author_sort | Chengbin Huang |
| collection | DOAJ |
| description | Abstract Introduction A large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral body and skeletal muscle for opportunistic screening in patients with osteoporosis. Materials and methods Our team retrospectively collected clinical information from participants who underwent unenhanced chest CT and dual-energy X-ray absorptiometry (DXA) examinations between January 1, 2022, and December 31, 2022, at four hospitals. These participants were divided into a training set (n = 581), an external test set 1 (n = 229), an external test set 2 (n = 198) and an external test set 3 (n = 118). Five CNN models were constructed based on chest CT images to screen patients with osteoporosis and compared with the SMI model to predict the performance of osteoporosis patients. Results All CNN models have good performance in predicting osteoporosis patients. The average F1 score of Densenet121 in the three external test sets was 0.865. The area under the curve (AUC) of Desenet121 in external test set 1, external test set 2, and external test set 3 were 0.827, 0.859, and 0.865, respectively. Furthermore, the Densenet121 model demonstrated a notably superior performance compared to the SMI model in predicting osteoporosis patients. Conclusions The CNN model based on unenhanced chest CT vertebral and skeletal muscle images can opportunistically screen patients with osteoporosis. Clinicians can use the CNN model to intervene in patients with osteoporosis and promptly avoid fragility fractures. Critical relevance statement The CNN model based on unenhanced chest CT vertebral and skeletal muscle images can opportunistically screen patients with osteoporosis. Clinicians can use the CNN model to intervene in patients with osteoporosis and promptly avoid fragility fractures. Key Points The application of unenhanced chest CT is increasing. Most people do not consciously use DXA to screen themselves for osteoporosis. A deep learning model was constructed based on CT images from four institutions. Graphical Abstract |
| format | Article |
| id | doaj-art-19e457befcc04019b4ed5b574966525c |
| institution | Kabale University |
| issn | 1869-4101 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj-art-19e457befcc04019b4ed5b574966525c2025-01-12T12:26:34ZengSpringerOpenInsights into Imaging1869-41012025-01-0116111110.1186/s13244-024-01817-2Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort studyChengbin Huang0Dengying Wu1Bingzhang Wang2Chenxuan Hong3Jiasen Hu4Zijian Yan5Jianpeng Chen6Yaping Jin7Yingze Zhang8Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical UniversityDepartment of Orthopaedics, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical UniversityDepartment of Orthopaedics, Wenzhou Hospital of Integrated Traditional Chinese and Western MedicineDepartment of Orthopaedics, People’s Hospital of CangnanDepartment of Orthopaedics, Yueqing People’s HospitalDepartment of Orthopaedics, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical UniversitySchool of Medicine, Nankai UniversityDepartment of Orthopaedics, Yueqing People’s HospitalDepartment of Orthopaedics, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical UniversityAbstract Introduction A large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral body and skeletal muscle for opportunistic screening in patients with osteoporosis. Materials and methods Our team retrospectively collected clinical information from participants who underwent unenhanced chest CT and dual-energy X-ray absorptiometry (DXA) examinations between January 1, 2022, and December 31, 2022, at four hospitals. These participants were divided into a training set (n = 581), an external test set 1 (n = 229), an external test set 2 (n = 198) and an external test set 3 (n = 118). Five CNN models were constructed based on chest CT images to screen patients with osteoporosis and compared with the SMI model to predict the performance of osteoporosis patients. Results All CNN models have good performance in predicting osteoporosis patients. The average F1 score of Densenet121 in the three external test sets was 0.865. The area under the curve (AUC) of Desenet121 in external test set 1, external test set 2, and external test set 3 were 0.827, 0.859, and 0.865, respectively. Furthermore, the Densenet121 model demonstrated a notably superior performance compared to the SMI model in predicting osteoporosis patients. Conclusions The CNN model based on unenhanced chest CT vertebral and skeletal muscle images can opportunistically screen patients with osteoporosis. Clinicians can use the CNN model to intervene in patients with osteoporosis and promptly avoid fragility fractures. Critical relevance statement The CNN model based on unenhanced chest CT vertebral and skeletal muscle images can opportunistically screen patients with osteoporosis. Clinicians can use the CNN model to intervene in patients with osteoporosis and promptly avoid fragility fractures. Key Points The application of unenhanced chest CT is increasing. Most people do not consciously use DXA to screen themselves for osteoporosis. A deep learning model was constructed based on CT images from four institutions. Graphical Abstracthttps://doi.org/10.1186/s13244-024-01817-2OsteoporosisChest CTDeep learningConvolutional neural networkMulticenter cohort study |
| spellingShingle | Chengbin Huang Dengying Wu Bingzhang Wang Chenxuan Hong Jiasen Hu Zijian Yan Jianpeng Chen Yaping Jin Yingze Zhang Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort study Insights into Imaging Osteoporosis Chest CT Deep learning Convolutional neural network Multicenter cohort study |
| title | Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort study |
| title_full | Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort study |
| title_fullStr | Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort study |
| title_full_unstemmed | Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort study |
| title_short | Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort study |
| title_sort | application of deep learning model based on unenhanced chest ct for opportunistic screening of osteoporosis a multicenter retrospective cohort study |
| topic | Osteoporosis Chest CT Deep learning Convolutional neural network Multicenter cohort study |
| url | https://doi.org/10.1186/s13244-024-01817-2 |
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