Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke
Abstract To validate the clinical feasibility of deep learning-driven magnetic resonance angiography (DL-driven MRA) collateral map in acute ischemic stroke. We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-Net, to generate DL-driven MRA collateral...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-85731-7 |
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| author | Ye Jin Jeon Hong Gee Roh Sumin Jung Hyun Yang Hee Jong Ki Jeong Jin Park Taek-Jun Lee Na Il Shin Ji Sung Lee Jin Tae Kwak Hyun Jeong Kim |
| author_facet | Ye Jin Jeon Hong Gee Roh Sumin Jung Hyun Yang Hee Jong Ki Jeong Jin Park Taek-Jun Lee Na Il Shin Ji Sung Lee Jin Tae Kwak Hyun Jeong Kim |
| author_sort | Ye Jin Jeon |
| collection | DOAJ |
| description | Abstract To validate the clinical feasibility of deep learning-driven magnetic resonance angiography (DL-driven MRA) collateral map in acute ischemic stroke. We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-Net, to generate DL-driven MRA collateral maps. Two raters graded the collateral perfusion scores of both conventional and DL-driven MRA collateral maps and measured the grading time. They also qualitatively assessed the image quality of both collateral maps. Interrater and inter-method agreements for collateral perfusion grading between the two collateral maps were analyzed, along with a comparison of grading time and image quality. In the analysis of the 296 acute ischemic stroke patients, the inter-method agreement for collateral perfusion grading was almost perfect (κ = 0.96, 95% CI: 0.95–0.98). Compared to conventional MRA collateral maps, the time taken for collateral perfusion grading on DL-driven MRA collateral maps was shorter (P < 0.001 for rater 1 and P = 0.003 for rater 2), and the image quality of the DL-driven MRA collateral maps was superior (P < 0.001 for rater 1 and P = 0.002 for rater 2). The DL-driven MRA collateral map demonstrates clinical feasibility for collateral perfusion grading in acute ischemic stroke, with the added benefits of reduced generation and interpretation time, along with improved image quality of the MRA collateral map. |
| format | Article |
| id | doaj-art-25b86ae0981048b5a7b41041a2d34fd9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-25b86ae0981048b5a7b41041a2d34fd92025-01-19T12:17:01ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-025-85731-7Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic strokeYe Jin Jeon0Hong Gee Roh1Sumin Jung2Hyun Yang3Hee Jong Ki4Jeong Jin Park5Taek-Jun Lee6Na Il Shin7Ji Sung Lee8Jin Tae Kwak9Hyun Jeong Kim10Department of Computer Science, University of CaliforniaDepartment of Radiology, Konkuk University Medical Center, Konkuk University School of MedicineSchool of Electrical Engineering, Korea UniversitySchool of Electrical Engineering, Korea UniversityDepartment of Neurosurgery, Daejeon St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDepartment of Neurology, Konkuk University Medical Center, Konkuk University School of MedicineDepartment of Neurology, Daejeon St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDepartment of Neurosurgery, Daejeon St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaClinical Research Center, Asan Institute for Life Science, Asan Medical Center, University of Ulsan College of MedicineDeepClue Inc.DeepClue Inc.Abstract To validate the clinical feasibility of deep learning-driven magnetic resonance angiography (DL-driven MRA) collateral map in acute ischemic stroke. We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-Net, to generate DL-driven MRA collateral maps. Two raters graded the collateral perfusion scores of both conventional and DL-driven MRA collateral maps and measured the grading time. They also qualitatively assessed the image quality of both collateral maps. Interrater and inter-method agreements for collateral perfusion grading between the two collateral maps were analyzed, along with a comparison of grading time and image quality. In the analysis of the 296 acute ischemic stroke patients, the inter-method agreement for collateral perfusion grading was almost perfect (κ = 0.96, 95% CI: 0.95–0.98). Compared to conventional MRA collateral maps, the time taken for collateral perfusion grading on DL-driven MRA collateral maps was shorter (P < 0.001 for rater 1 and P = 0.003 for rater 2), and the image quality of the DL-driven MRA collateral maps was superior (P < 0.001 for rater 1 and P = 0.002 for rater 2). The DL-driven MRA collateral map demonstrates clinical feasibility for collateral perfusion grading in acute ischemic stroke, with the added benefits of reduced generation and interpretation time, along with improved image quality of the MRA collateral map.https://doi.org/10.1038/s41598-025-85731-7Cerebrovascular disordersStrokeCollateral circulationDeep learningMagnetic resonance imagingArtificial intelligence |
| spellingShingle | Ye Jin Jeon Hong Gee Roh Sumin Jung Hyun Yang Hee Jong Ki Jeong Jin Park Taek-Jun Lee Na Il Shin Ji Sung Lee Jin Tae Kwak Hyun Jeong Kim Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke Scientific Reports Cerebrovascular disorders Stroke Collateral circulation Deep learning Magnetic resonance imaging Artificial intelligence |
| title | Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke |
| title_full | Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke |
| title_fullStr | Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke |
| title_full_unstemmed | Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke |
| title_short | Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke |
| title_sort | clinical feasibility of deep learning driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke |
| topic | Cerebrovascular disorders Stroke Collateral circulation Deep learning Magnetic resonance imaging Artificial intelligence |
| url | https://doi.org/10.1038/s41598-025-85731-7 |
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