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Dong F, Song J, Chen B, Xie X, Cheng J, Song J, Huang Q. Improved detection of aortic dissection in non-contrast-enhanced chest CT using an attention-based deep learning model. Heliyon 2024; 10:e24547. [PMID: 38304839 PMCID: PMC10831773 DOI: 10.1016/j.heliyon.2024.e24547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 12/22/2023] [Accepted: 01/10/2024] [Indexed: 02/03/2024] Open
Abstract
Rationale and objectives This study investigated the effects of implementing an attention-based deep learning model for the detection of aortic dissection (AD) using non-contrast-enhanced chest computed tomography (CT). Materials and methods We analysed the records of 1300 patients who underwent contrast-enhanced chest CT at 2 medical centres between January 2015 and February 2023. We considered an internal cohort of 200 patients with AD and 200 patients without AD and an external test cohort of 40 patients with AD and 40 patients without AD. The internal cohort was divided into training and test sets, and a deep learning model was trained using 9600 CT images. A convolutional block attention module (CBAM) and a traditional deep learning architecture (namely, You Only Look Once version 5 [YOLOv5]) were combined into an attention-based model (i.e., YOLOv5-CBAM). Its performance was measured against the unmodified YOLOv5 model, and the accuracy, sensitivity, and specificity of the algorithm were evaluated by two independent radiologists. Results The CBAM-based model outperformed the traditional deep learning model. In the external testing set, YOLOv5-CBAM achieved an area under the curve (AUC) of 0.938, accuracy of 91.5 %, sensitivity of 90.0 %, and specificity of 92.9 %, whereas the unmodified model achieved an AUC of 0.844, accuracy of 83.6 %, sensitivity of 71.2 %, and specificity of 96.0 %. The sensitivity results of the unmodified algorithms were not significantly different from those of the radiologists; however, the proposed YOLOv5-CBAM algorithm outperformed the unmodified algorithms in terms of detection. Conclusions Incorporating the CBAM attention mechanism into a deep learning model can significantly improve AD detection in non-contrast-enhanced chest CT. This approach may aid radiologists in the timely and accurate diagnosis of AD, which is important for improving patient outcomes.
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Affiliation(s)
- Fenglei Dong
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, 1111 east section of Wenzhou avenue, Longwan District, Wenzhou, China
| | - Jiao Song
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, 1111 east section of Wenzhou avenue, Longwan District, Wenzhou, China
| | - Bo Chen
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, 1111 east section of Wenzhou avenue, Longwan District, Wenzhou, China
| | - Xiaoxiao Xie
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, 1111 east section of Wenzhou avenue, Longwan District, Wenzhou, China
| | - Jianmin Cheng
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, 1111 east section of Wenzhou avenue, Longwan District, Wenzhou, China
| | - Jiawen Song
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, 1111 east section of Wenzhou avenue, Longwan District, Wenzhou, China
| | - Qun Huang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, No. 1 Fanhai West Road, Ouhai District, Wenzhou, China
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Yi Y, Mao L, Wang C, Guo Y, Luo X, Jia D, Lei Y, Pan J, Li J, Li S, Li XL, Jin Z, Wang Y. Advanced Warning of Aortic Dissection on Non-Contrast CT: The Combination of Deep Learning and Morphological Characteristics. Front Cardiovasc Med 2022; 8:762958. [PMID: 35071345 PMCID: PMC8767113 DOI: 10.3389/fcvm.2021.762958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/24/2021] [Indexed: 12/02/2022] Open
Abstract
Background: The identification of aortic dissection (AD) at baseline plays a crucial role in clinical practice. Non-contrast CT scans are widely available, convenient, and easy to perform. However, the detection of AD on non-contrast CT scans by radiologists currently lacks sensitivity and is suboptimal. Methods: A total of 452 patients who underwent aortic CT angiography (CTA) were enrolled retrospectively from two medical centers in China to form the internal cohort (341 patients, 139 patients with AD, 202 patients with non-AD) and the external testing cohort (111 patients, 46 patients with AD, 65 patients with non-AD). The internal cohort was divided into the training cohort (n = 238), validation cohort (n = 35), and internal testing cohort (n = 68). Morphological characteristics were extracted from the aortic segmentation. A deep-integrated model based on the Gaussian Naive Bayes algorithm was built to differentiate AD from non-AD, using the combination of the three-dimensional (3D) deep-learning model score and morphological characteristics. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to evaluate the model performance. The proposed model was also compared with the subjective assessment of radiologists. Results: After the combination of all the morphological characteristics, our proposed deep-integrated model significantly outperformed the 3D deep-learning model (AUC: 0.948 vs. 0.803 in the internal testing cohort and 0.969 vs. 0.814 in the external testing cohort, both p < 0.05). The accuracy, sensitivity, and specificity of our model reached 0.897, 0.862, and 0.923 in the internal testing cohort and 0.730, 0.978, and 0.554 in the external testing cohort, respectively. The accuracy for AD detection showed no significant difference between our model and the radiologists (p > 0.05). Conclusion: The proposed model presented good performance for AD detection on non-contrast CT scans; thus, early diagnosis and prompt treatment would be available.
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Affiliation(s)
- Yan Yi
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Li Mao
- AI Lab, Deepwise Healthcare, Beijing, China
| | - Cheng Wang
- AI Lab, Deepwise Healthcare, Beijing, China
| | - Yubo Guo
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiao Luo
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | | | - Yi Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Judong Pan
- Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, United States
| | - Jiayue Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Shufang Li
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiu-Li Li
- AI Lab, Deepwise Healthcare, Beijing, China
| | - Zhengyu Jin
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
- Zhengyu Jin
| | - Yining Wang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
- *Correspondence: Yining Wang
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Das D, Kumar A, Singh J, Pramanik S. Fluoroscopic “calcium sign” or reverse “c” sign of the aortic knuckle in a case of chronic total occlusion of left anterior descending coronary artery. Res Cardiovasc Med 2021. [DOI: 10.4103/rcm.rcm_52_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Sathiadoss P, Haroon M, Wongwaisayawan S, Krishna S, Sheikh AM. Multidetector Computed Tomography in Traumatic and Nontraumatic Aortic Emergencies: Emphasis on Acute Aortic Syndromes. Can Assoc Radiol J 2020; 71:322-334. [PMID: 32106708 DOI: 10.1177/0846537120902069] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Aortic emergencies comprise of a list of conditions which are uncommon but are potentially fatal. Prognosis is usually determined by emergent diagnosis and treatment and hence radiology plays a key role in patient management. In this article, we aim to review the various causes of aortic emergencies and the relevant imaging findings placing special emphasis on acute aortic syndromes.
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Affiliation(s)
- Paul Sathiadoss
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ontario, Canada
| | - Mohammad Haroon
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ontario, Canada
| | - Sirote Wongwaisayawan
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ontario, Canada.,Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Satheesh Krishna
- Joint Department of Medical Imaging, Toronto General Hospital, University of Toronto, Ontario, Canada
| | - Adnan M Sheikh
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ontario, Canada
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Haensig M. Type B intramural hematoma: focus on reasons for development and overlapping clinical disease. Ann Cardiothorac Surg 2019; 8:494-496. [PMID: 31463214 DOI: 10.21037/acs.2019.06.04] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Martin Haensig
- Department of Vascular Surgery, University of Leipzig, Leipzig, Germany
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High intimal flap mobility assessed by intravascular ultrasound is associated with better short-term results after TEVAR in chronic aortic dissection. Sci Rep 2019; 9:7267. [PMID: 31086282 PMCID: PMC6513991 DOI: 10.1038/s41598-019-43856-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 05/02/2019] [Indexed: 01/16/2023] Open
Abstract
Thoracic endovascular aortic repair (TEVAR) in chronic aortic dissection remains controversial. We analysed whether a high intimal flap mobility (IFM) of the dissection membrane has an impact on aortic remodelling after TEVAR in chronic Type B aortic dissection. Patients undergoing TEVAR with intravascular ultrasound (IVUS) were analysed and IFM was calculated. High IFM was defined as maximum flap amplitude >3 mm. For determining aortic remodelling, the degree of true lumen (TL) expansion was analysed in the last available follow-up CT. Fifty-two patients (63.6 ± 15.4 years) with a mean follow-up of 26.6 ± 20.7 months were analysed. The mobile flap group (n = 29) showed higher absolute TL expansion at the distal stent-graft (5.9 ± 3.1 vs. 3.3 ± 5.4 mm; p = 0.036) and a higher increase in TL diameter (18 ± 10 vs. 9 ± 15%; p = 0.017) compared to the non-mobile group (n = 23). Basic TEVAR-related outcome characteristics were comparable, but the mobile intimal flap group showed a lower re-intervention rate (3 vs. 8pts.; p = 0.032) in chronic dissections. High IFM in chronic Type B aortic dissection is linked to improved aortic remodelling and is associated with a lower re-intervention rate over time. IVUS assessment of IFM in chronic Type B aortic dissection might be helpful in identifying patients with better remodelling after TEVAR.
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Meng J, Mellnick VM, Monteiro S, Patlas MN. Acute Aortic Syndrome: Yield of Computed Tomography Angiography in Patients With Acute Chest Pain. Can Assoc Radiol J 2019; 70:23-28. [PMID: 30691558 DOI: 10.1016/j.carj.2018.10.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 10/07/2018] [Accepted: 10/08/2018] [Indexed: 01/12/2023] Open
Affiliation(s)
- Jane Meng
- Department of Radiology, McMaster University, Hamilton, Ontario, Canada
| | | | - Sandra Monteiro
- Department of Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada
| | - Michael N Patlas
- Department of Radiology, McMaster University, Hamilton, Ontario, Canada.
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Oderich GS, Kärkkäinen JM, Reed NR, Tenorio ER, Sandri GA. Penetrating Aortic Ulcer and Intramural Hematoma. Cardiovasc Intervent Radiol 2018; 42:321-334. [DOI: 10.1007/s00270-018-2114-x] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 11/01/2018] [Indexed: 01/10/2023]
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Lortz J, Tsagakis K, Rammos C, Horacek M, Schlosser T, Jakob H, Rassaf T, Jánosi RA. Intravascular ultrasound assisted sizing in thoracic endovascular aortic repair improves aortic remodeling in Type B aortic dissection. PLoS One 2018; 13:e0196180. [PMID: 29672613 PMCID: PMC5908162 DOI: 10.1371/journal.pone.0196180] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Accepted: 04/06/2018] [Indexed: 01/16/2023] Open
Abstract
The precise sizing of the stent graft in thoracic endovascular aortic repair (TEVAR) affects aortic remodeling and hence, further outcome. Covering the proximal entry tear is essential for successful treatment of Type B aortic dissection. Intravascular ultrasound (IVUS) enables real-time aortic diameter assessment, and is especially useful when computed tomography (CT) image quality is poor. IVUS, however, is not routinely utilized due to cost inefficiency. We investigated the impact of IVUS-assisted stent graft sizing on aortic remodeling in TEVAR. In this single-center retrospective study we evaluated patients with Type B aortic dissection undergoing both CT and IVUS before TEVAR. We assessed the aortic diameter at the level of the left subclavian artery via both methods before stent implantation and analyzed due to which method the implanted stent graft was chosen, retrospectively. To determine the degrees of aortic remodeling involved, we evaluated true lumen and false lumen diameters, and total aortic remodeling in CT. We analyzed 45 patients with Type B aortic dissection undergoing TEVAR. The mean ages were 66.9±10.0 years fo0072 IVUS (n = 20) and 62.3±14.2 years for CT-assisted TEVAR (n = 25; p = 0.226). The follow-up time for both groups did not differ between the two groups (IVUS: 22.9±23.1 months, CT: 25.6±23.0 months; p = 0.700). While both methods were associated with advantages regarding aortic remodeling, IVUS-assisted sizing yielded a greater increase in true lumen (30.4±6.2 vs. 25.6±5.3; p = 0.008) and reductions in false lumen (14.4±8.5 vs. 23.9±9.3; p = 0.001) and total aortic diameter (35.5±6.0 vs. 39.9±8.1; p = 0.045). IVUS-guided stent graft sizing in Type B aortic dissection shows beneficial effects on aortic remodeling and might be of additional advantage in aortic diseases, especially when CT image quality is poor.
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Affiliation(s)
- Julia Lortz
- Department of Cardiology and Vascular Medicine, West-German Heart and Vascular Center Essen, University of Duisburg-Essen, Essen, Germany
| | - Konstantinos Tsagakis
- Department of Thoracic and Cardiovascular Surgery, West-German Heart and Vascular Center Essen, University of Duisburg-Essen, Essen, Germany
| | - Christos Rammos
- Department of Cardiology and Vascular Medicine, West-German Heart and Vascular Center Essen, University of Duisburg-Essen, Essen, Germany
| | - Michael Horacek
- Department of Cardiology and Vascular Medicine, West-German Heart and Vascular Center Essen, University of Duisburg-Essen, Essen, Germany
| | - Thomas Schlosser
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Heinz Jakob
- Department of Thoracic and Cardiovascular Surgery, West-German Heart and Vascular Center Essen, University of Duisburg-Essen, Essen, Germany
| | - Tienush Rassaf
- Department of Cardiology and Vascular Medicine, West-German Heart and Vascular Center Essen, University of Duisburg-Essen, Essen, Germany
| | - Rolf Alexander Jánosi
- Department of Cardiology and Vascular Medicine, West-German Heart and Vascular Center Essen, University of Duisburg-Essen, Essen, Germany
- * E-mail:
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DeWeert D, Lovell E, Patel S. Computed tomography angiography-negative aortic dissection in a patient using Phencyclidine. World J Emerg Med 2018; 9:144-148. [PMID: 29576829 PMCID: PMC5847502 DOI: 10.5847/wjem.j.1920-8642.2018.02.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 10/26/2017] [Indexed: 01/16/2023] Open
Affiliation(s)
- Daniel DeWeert
- Advocate Christ Medical Center, 4440 95th St, Oak Lawn, IL 60453, USA
| | - Elise Lovell
- Advocate Christ Medical Center, 4440 95th St, Oak Lawn, IL 60453, USA
| | - Samir Patel
- Advocate Christ Medical Center, 4440 95th St, Oak Lawn, IL 60453, USA
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