Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification
Abstract Background Coronary computed tomography angiography examinations are increasingly becoming established as a minimally invasive method for diagnosing coronary diseases. However, although various imaging and processing methods have been developed, coronary artery calcification remains a major...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-05-01
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| Series: | The Egyptian Heart Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43044-022-00280-y |
| _version_ | 1811338856099741696 |
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| author | Hidekazu Inage Nobuo Tomizawa Yujiro Otsuka Chihiro Aoshima Yuko Kawaguchi Kazuhisa Takamura Rie Matsumori Yuki Kamo Yui Nozaki Daigo Takahashi Ayako Kudo Makoto Hiki Yosuke Kogure Shinichiro Fujimoto Tohru Minamino Shigeki Aoki |
| author_facet | Hidekazu Inage Nobuo Tomizawa Yujiro Otsuka Chihiro Aoshima Yuko Kawaguchi Kazuhisa Takamura Rie Matsumori Yuki Kamo Yui Nozaki Daigo Takahashi Ayako Kudo Makoto Hiki Yosuke Kogure Shinichiro Fujimoto Tohru Minamino Shigeki Aoki |
| author_sort | Hidekazu Inage |
| collection | DOAJ |
| description | Abstract Background Coronary computed tomography angiography examinations are increasingly becoming established as a minimally invasive method for diagnosing coronary diseases. However, although various imaging and processing methods have been developed, coronary artery calcification remains a major limitation in the evaluation of the vascular lumen. Subtraction coronary computed tomography angiography (Sub-CCTA) is a method known to be able to reduce the influence of coronary artery calcification and is therefore feasible for improving the diagnosis of significant stenosis in patients with severe calcification. However, Sub-CCTA still involves some problems, such as the increased radiation dose due to plain (mask) imaging, extended breath-holding time, and misregistration due to differences in the imaging phase. Therefore, we considered using artificial intelligence instead of Sub-CCTA to visualize the coronary lumen with high calcification. Given this background, the present study aimed to evaluate the diagnostic performance of a deep learning-based lumen extraction method (DL-LEM) to detect significant stenosis on CCTA in 99 consecutive patients (891 segments) with severe coronary calcification from November 2015 to March 2018. We also estimated the impact of DL-LEM on the medical economics in Japan. Results The DL-LEM slightly improved the per-segment diagnostic accuracy from 74.5 to 76.4%, and the area under the curve (AUC) slightly improved from 0.752 to 0.767 (p = 0.030). When analyzing the 228 segments that could not be evaluated because of severe calcification on the original CCTA images, the DL-LEM improved the accuracy from 35.5 to 42.5%, and the AUC improved from 0.500 to 0.587 (p = 0.00018). As a result, DL-LEM analysis could have avoided invasive coronary angiography in 4/99 cases (per patient). From the calculated results, it was estimated that the number of exams that can be avoided in Japan in one year is approximately 747 for invasive coronary angiography, 219 for fractional flow reserve, and 248 for nuclear exam. The total amount of medical fee that could be reduced was 225,629,368 JPY. Conclusions These findings suggest that the DL-LEM may improve the diagnostic performance in detecting significant stenosis in patients with severe coronary calcification. In addition, the results suggest that not a small medical economic effect can be expected. |
| first_indexed | 2024-04-13T18:17:42Z |
| format | Article |
| id | doaj.art-280faa856511493ab89358dfdc0ae934 |
| institution | Directory Open Access Journal |
| issn | 2090-911X |
| language | English |
| last_indexed | 2024-04-13T18:17:42Z |
| publishDate | 2022-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | The Egyptian Heart Journal |
| spelling | doaj.art-280faa856511493ab89358dfdc0ae9342022-12-22T02:35:37ZengSpringerOpenThe Egyptian Heart Journal2090-911X2022-05-0174111110.1186/s43044-022-00280-yUse of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcificationHidekazu Inage0Nobuo Tomizawa1Yujiro Otsuka2Chihiro Aoshima3Yuko Kawaguchi4Kazuhisa Takamura5Rie Matsumori6Yuki Kamo7Yui Nozaki8Daigo Takahashi9Ayako Kudo10Makoto Hiki11Yosuke Kogure12Shinichiro Fujimoto13Tohru Minamino14Shigeki Aoki15Department of Radiology, Graduate School of Medicine, Juntendo UniversityDepartment of Radiology, Graduate School of Medicine, Juntendo UniversityDepartment of Radiology, Graduate School of Medicine, Juntendo UniversityDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Radiological Technology, Juntendo University HospitalDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Cardiovascular Biology and Medicine, Graduate School of Medicine, Juntendo UniversityDepartment of Radiology, Graduate School of Medicine, Juntendo UniversityAbstract Background Coronary computed tomography angiography examinations are increasingly becoming established as a minimally invasive method for diagnosing coronary diseases. However, although various imaging and processing methods have been developed, coronary artery calcification remains a major limitation in the evaluation of the vascular lumen. Subtraction coronary computed tomography angiography (Sub-CCTA) is a method known to be able to reduce the influence of coronary artery calcification and is therefore feasible for improving the diagnosis of significant stenosis in patients with severe calcification. However, Sub-CCTA still involves some problems, such as the increased radiation dose due to plain (mask) imaging, extended breath-holding time, and misregistration due to differences in the imaging phase. Therefore, we considered using artificial intelligence instead of Sub-CCTA to visualize the coronary lumen with high calcification. Given this background, the present study aimed to evaluate the diagnostic performance of a deep learning-based lumen extraction method (DL-LEM) to detect significant stenosis on CCTA in 99 consecutive patients (891 segments) with severe coronary calcification from November 2015 to March 2018. We also estimated the impact of DL-LEM on the medical economics in Japan. Results The DL-LEM slightly improved the per-segment diagnostic accuracy from 74.5 to 76.4%, and the area under the curve (AUC) slightly improved from 0.752 to 0.767 (p = 0.030). When analyzing the 228 segments that could not be evaluated because of severe calcification on the original CCTA images, the DL-LEM improved the accuracy from 35.5 to 42.5%, and the AUC improved from 0.500 to 0.587 (p = 0.00018). As a result, DL-LEM analysis could have avoided invasive coronary angiography in 4/99 cases (per patient). From the calculated results, it was estimated that the number of exams that can be avoided in Japan in one year is approximately 747 for invasive coronary angiography, 219 for fractional flow reserve, and 248 for nuclear exam. The total amount of medical fee that could be reduced was 225,629,368 JPY. Conclusions These findings suggest that the DL-LEM may improve the diagnostic performance in detecting significant stenosis in patients with severe coronary calcification. In addition, the results suggest that not a small medical economic effect can be expected.https://doi.org/10.1186/s43044-022-00280-yDeep learningCycle-GANCoronary CT angiography (CCTA)Coronary artery calcification |
| spellingShingle | Hidekazu Inage Nobuo Tomizawa Yujiro Otsuka Chihiro Aoshima Yuko Kawaguchi Kazuhisa Takamura Rie Matsumori Yuki Kamo Yui Nozaki Daigo Takahashi Ayako Kudo Makoto Hiki Yosuke Kogure Shinichiro Fujimoto Tohru Minamino Shigeki Aoki Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification The Egyptian Heart Journal Deep learning Cycle-GAN Coronary CT angiography (CCTA) Coronary artery calcification |
| title | Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification |
| title_full | Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification |
| title_fullStr | Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification |
| title_full_unstemmed | Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification |
| title_short | Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification |
| title_sort | use of a deep learning based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification |
| topic | Deep learning Cycle-GAN Coronary CT angiography (CCTA) Coronary artery calcification |
| url | https://doi.org/10.1186/s43044-022-00280-y |
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