Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning
Background/Objectives: To develop and validate a model system using deep learning algorithms for the automatic detection of type A aortic dissection (AD), and differentiate it from normal and type B AD patients. Methods: In this retrospective study, a deep learning model is developed, based on aorti...
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MDPI AG
2024-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/1/12 |
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| author | Hung-Hsien Liu Chun-Bi Chang Yi-Sa Chen Chang-Fu Kuo Chun-Yu Lin Cheng-Yu Ma Li-Jen Wang |
| author_facet | Hung-Hsien Liu Chun-Bi Chang Yi-Sa Chen Chang-Fu Kuo Chun-Yu Lin Cheng-Yu Ma Li-Jen Wang |
| author_sort | Hung-Hsien Liu |
| collection | DOAJ |
| description | Background/Objectives: To develop and validate a model system using deep learning algorithms for the automatic detection of type A aortic dissection (AD), and differentiate it from normal and type B AD patients. Methods: In this retrospective study, a deep learning model is developed, based on aortic computed tomography angiography (CTA) scans of 498 patients using training, validation and test sets of 398, 50 and 50 patients, respectively. An independent test set of 316 patients is used to validate and evaluate its performance. Results: Our model comprises two components. The first one is an objection detection model, which can identify the aorta from CTA. The second one is a dissection classification model, which can automatically detect the presence of aortic dissection and determine its type based on Stanford classification. Overall, the sensitivity and specificity for Type A AD were 0.969 and 0.982, for Type B AD were 0.946 and 0.996 and for normal cases were 0.988 and 1.000, respectively. The average processing time per CTA scan was 7.9 ± 2.8 s. (mean ± standard deviation). Conclusions: This deep learning automatic model can accurately and quickly detect type A AD patients, and could serve as an imaging triage in an emergency setting and facilitate early intervention and surgery to decrease the mortality rates of type A AD patients. |
| format | Article |
| id | doaj-art-fbcfc05823bb4cec8e23b14848ac3672 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-fbcfc05823bb4cec8e23b14848ac36722025-01-10T13:16:27ZengMDPI AGDiagnostics2075-44182024-12-011511210.3390/diagnostics15010012Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep LearningHung-Hsien Liu0Chun-Bi Chang1Yi-Sa Chen2Chang-Fu Kuo3Chun-Yu Lin4Cheng-Yu Ma5Li-Jen Wang6Department of Medical Imaging and Intervention, New Taipei City Municipal Tucheng Hospital, New Taipei City 236043, TaiwanDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan City 333423, TaiwanDepartment of Medical Imaging and Intervention, New Taipei City Municipal Tucheng Hospital, New Taipei City 236043, TaiwanCenter for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan City 333423, TaiwanDepartment of Medicine, College of Medicine, Chang Gung University, Taoyuan City 333323, TaiwanDepartment of Artificial Intelligence, Chang Gung University, Taoyuan City 333323, TaiwanDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan City 333423, TaiwanBackground/Objectives: To develop and validate a model system using deep learning algorithms for the automatic detection of type A aortic dissection (AD), and differentiate it from normal and type B AD patients. Methods: In this retrospective study, a deep learning model is developed, based on aortic computed tomography angiography (CTA) scans of 498 patients using training, validation and test sets of 398, 50 and 50 patients, respectively. An independent test set of 316 patients is used to validate and evaluate its performance. Results: Our model comprises two components. The first one is an objection detection model, which can identify the aorta from CTA. The second one is a dissection classification model, which can automatically detect the presence of aortic dissection and determine its type based on Stanford classification. Overall, the sensitivity and specificity for Type A AD were 0.969 and 0.982, for Type B AD were 0.946 and 0.996 and for normal cases were 0.988 and 1.000, respectively. The average processing time per CTA scan was 7.9 ± 2.8 s. (mean ± standard deviation). Conclusions: This deep learning automatic model can accurately and quickly detect type A AD patients, and could serve as an imaging triage in an emergency setting and facilitate early intervention and surgery to decrease the mortality rates of type A AD patients.https://www.mdpi.com/2075-4418/15/1/12deep learningaortic dissectioncomputed tomography |
| spellingShingle | Hung-Hsien Liu Chun-Bi Chang Yi-Sa Chen Chang-Fu Kuo Chun-Yu Lin Cheng-Yu Ma Li-Jen Wang Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning Diagnostics deep learning aortic dissection computed tomography |
| title | Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning |
| title_full | Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning |
| title_fullStr | Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning |
| title_full_unstemmed | Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning |
| title_short | Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning |
| title_sort | automated detection and differentiation of stanford type a and type b aortic dissections in cta scans using deep learning |
| topic | deep learning aortic dissection computed tomography |
| url | https://www.mdpi.com/2075-4418/15/1/12 |
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