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Qiu J, Tan G, Lin Y, Guan J, Dai Z, Wang F, Zhuang C, Wilman AH, Huang H, Cao Z, Tang Y, Jia Y, Li Y, Zhou T, Wu R. Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: A preliminary study based on deep learning. Magn Reson Imaging 2022; 94:105-111. [PMID: 36174873 DOI: 10.1016/j.mri.2022.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 09/12/2022] [Accepted: 09/22/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Intracranial atherosclerotic stenosis of a major intracranial artery is the common cause of ischemic stroke. We evaluate the feasibility of using deep learning to automatically detect intracranial arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography. METHODS In a retrospective study, magnetic resonance images with radiological reports of intracranial arterial stenosis and occlusion were extracted. The images were randomly divided into a training set and a test set. The manual annotation of lesions with a bounding box labeled "moderate stenosis," "severe stenosis," "occlusion," and "absence of signal" was considered as ground truth. A deep learning algorithm based on you only look once version 5 (YOLOv5) detection model was developed with the training set, and its sensitivity and positive predictive values to detect lesions were evaluated in the test set. RESULTS A dataset of 200 examinations consisted of a total of 411 lesions-242 moderate stenoses, 84 severe stenoses, 70 occlusions, and 15 absence of signal. The magnetic resonance images contained 291 lesions in the training set and 120 lesions in the test set. The sensitivity and positive predictive values were 64.2 and 83.7%, respectively. The detection sensitivity in relation to the location was greatest in the internal carotid artery (86.2%). CONCLUSIONS Applying deep learning algorithms in the automated detection of intracranial arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography is feasible and has great potential.
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Affiliation(s)
- Jinming Qiu
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China; Department of Radiology, the Sixth Affiliated Hospital, South China University of Technology, Foshan 528000, Guangdong, PR China
| | - Guanru Tan
- Department of Computer Science, Shantou University, Shantou 515041, Guangdong, PR China
| | - Yan Lin
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Jitian Guan
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Zhuozhi Dai
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Fei Wang
- Department of Computer Science, Shantou University, Shantou 515041, Guangdong, PR China
| | - Caiyu Zhuang
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Huaidong Huang
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Zhen Cao
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Yanyan Tang
- Department of Medical Imaging, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, Guangdong, PR China
| | - Yanlong Jia
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Yan Li
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
| | - Teng Zhou
- Department of Computer Science, Shantou University, Shantou 515041, Guangdong, PR China
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou 515800, China
| | - Renhua Wu
- Department of Radiology, the Second Affiliated Hospital, Shantou University Medical College, Shantou 515041, Guangdong, PR China
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