Artificial Intelligence-Assisted Detection of Osteoporotic Vertebral Fractures on Lateral Chest Radiographs in Post-Menopausal Women
Osteoporotic vertebral fractures (OVFs) are often not reported by radiologists on routine chest radiographs. This study aims to investigate the clinical value of a newly developed artificial intelligence (AI) tool, Ofeye 1.0, for automated detection of OVFs on lateral chest radiographs in post-menop...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
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MDPI AG
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/94001 |
| _version_ | 1848765825268318208 |
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| author | Silberstein, Jenna Wee, Cleo Gupta, Ashu Singh Ghotra, Switinder Seymour, H. Sá Dos Reis, Cláudia Zhang, Guicheng Sun, Zhonghua |
| author_facet | Silberstein, Jenna Wee, Cleo Gupta, Ashu Singh Ghotra, Switinder Seymour, H. Sá Dos Reis, Cláudia Zhang, Guicheng Sun, Zhonghua |
| author_sort | Silberstein, Jenna |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Osteoporotic vertebral fractures (OVFs) are often not reported by radiologists on routine chest radiographs. This study aims to investigate the clinical value of a newly developed artificial intelligence (AI) tool, Ofeye 1.0, for automated detection of OVFs on lateral chest radiographs in post-menopausal women (>60 years) who were referred to undergo chest x-rays for other reasons. A total of 510 de-identified lateral chest radiographs from three clinical sites were retrieved and analysed using the Ofeye 1.0 tool. These images were then reviewed by a consultant radiologist with findings serving as the reference standard for determining the diagnostic performance of the AI tool for the detection of OVFs. Of all the original radiologist reports, missed OVFs were found in 28.8% of images but were detected using the AI tool. The AI tool demonstrated high specificity of 92.8% (95% CI: 89.6, 95.2%), moderate accuracy of 80.3% (95% CI: 76.3, 80.4%), positive predictive value (PPV) of 73.7% (95% CI: 65.2, 80.8%), and negative predictive value (NPV) of 81.5% (95% CI: 79, 83.8%), but low sensitivity of 49% (95% CI: 40.7, 57.3%). The AI tool showed improved sensitivity compared with the original radiologist reports, which was 20.8% (95% CI: 14.5, 28.4). The new AI tool can be used as a complementary tool in routine diagnostic reports for the reduction in missed OVFs in elderly women. |
| first_indexed | 2025-11-14T11:41:24Z |
| format | Journal Article |
| id | curtin-20.500.11937-94001 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:41:24Z |
| publishDate | 2023 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-940012025-02-19T06:40:24Z Artificial Intelligence-Assisted Detection of Osteoporotic Vertebral Fractures on Lateral Chest Radiographs in Post-Menopausal Women Silberstein, Jenna Wee, Cleo Gupta, Ashu Singh Ghotra, Switinder Seymour, H. Sá Dos Reis, Cláudia Zhang, Guicheng Sun, Zhonghua Osteoporotic vertebral fractures (OVFs) are often not reported by radiologists on routine chest radiographs. This study aims to investigate the clinical value of a newly developed artificial intelligence (AI) tool, Ofeye 1.0, for automated detection of OVFs on lateral chest radiographs in post-menopausal women (>60 years) who were referred to undergo chest x-rays for other reasons. A total of 510 de-identified lateral chest radiographs from three clinical sites were retrieved and analysed using the Ofeye 1.0 tool. These images were then reviewed by a consultant radiologist with findings serving as the reference standard for determining the diagnostic performance of the AI tool for the detection of OVFs. Of all the original radiologist reports, missed OVFs were found in 28.8% of images but were detected using the AI tool. The AI tool demonstrated high specificity of 92.8% (95% CI: 89.6, 95.2%), moderate accuracy of 80.3% (95% CI: 76.3, 80.4%), positive predictive value (PPV) of 73.7% (95% CI: 65.2, 80.8%), and negative predictive value (NPV) of 81.5% (95% CI: 79, 83.8%), but low sensitivity of 49% (95% CI: 40.7, 57.3%). The AI tool showed improved sensitivity compared with the original radiologist reports, which was 20.8% (95% CI: 14.5, 28.4). The new AI tool can be used as a complementary tool in routine diagnostic reports for the reduction in missed OVFs in elderly women. 2023 Journal Article http://hdl.handle.net/20.500.11937/94001 10.3390/jcm12247730 http://creativecommons.org/licenses/by/4.0/ MDPI AG fulltext |
| spellingShingle | Silberstein, Jenna Wee, Cleo Gupta, Ashu Singh Ghotra, Switinder Seymour, H. Sá Dos Reis, Cláudia Zhang, Guicheng Sun, Zhonghua Artificial Intelligence-Assisted Detection of Osteoporotic Vertebral Fractures on Lateral Chest Radiographs in Post-Menopausal Women |
| title | Artificial Intelligence-Assisted Detection of Osteoporotic Vertebral Fractures on Lateral Chest Radiographs in Post-Menopausal Women |
| title_full | Artificial Intelligence-Assisted Detection of Osteoporotic Vertebral Fractures on Lateral Chest Radiographs in Post-Menopausal Women |
| title_fullStr | Artificial Intelligence-Assisted Detection of Osteoporotic Vertebral Fractures on Lateral Chest Radiographs in Post-Menopausal Women |
| title_full_unstemmed | Artificial Intelligence-Assisted Detection of Osteoporotic Vertebral Fractures on Lateral Chest Radiographs in Post-Menopausal Women |
| title_short | Artificial Intelligence-Assisted Detection of Osteoporotic Vertebral Fractures on Lateral Chest Radiographs in Post-Menopausal Women |
| title_sort | artificial intelligence-assisted detection of osteoporotic vertebral fractures on lateral chest radiographs in post-menopausal women |
| url | http://hdl.handle.net/20.500.11937/94001 |