Imaging-based outcome prediction in patients with intracerebral hemorrhage
Author:
Publisher
Springer Science and Business Media LLC
Subject
Neurology (clinical),Surgery
Link
http://link.springer.com/article/10.1007/s00701-018-3605-x/fulltext.html
Reference52 articles.
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3. Auer LM, Deinsberger W, Niederkorn K, Gell G, Kleinert R, Schneider G, Holzer P, Bone G, Mokry M, Korner E (1989) Endoscopic surgery versus medical treatment for spontaneous intracerebral hematoma: a randomized study. J Neurosurg. https://doi.org/10.3171/jns.1989.70.4.0530
4. Barras CD, Tress BM, Christensen S, MacGregor L, Collins M, Desmond PM, Skolnick BE, Mayer SA, Broderick JP, Diringer MN, Steiner T, Davis SM, Recombinant Activated Factor VII Intracerebral Hemorrhage Trial Investigators (2009) Density and shape as CT predictors of intracerebral hemorrhage growth. Stroke. https://doi.org/10.1161/STROKEAHA.108.536888
5. Blacquiere D, Demchuk AM, Al-Hazzaa M, Deshpande A, Petrcich W, Aviv RI, Rodriguez-Luna D, Molina CA, Silva Blas Y, Dzialowski I, Czlonkowska A, Boulanger JM, Lum C, Gubitz G, Padma V, Roy J, Kase CS, Bhatia R, Hill MD, Dowlatshahi D, PREDICT/Sunnybrook ICH CTA Study Group (2015) Intracerebral hematoma morphologic appearance on noncontrast computed tomography predicts significant hematoma expansion. Stroke. https://doi.org/10.1161/STROKEAHA.115.010566
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3. Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage;Journal of Clinical Medicine;2023-03-31
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