首页|Northeast Petroleum University Reports Findings in Stroke (Ischemic stroke outco me prediction with diversity features from whole brain tissue using deep learnin g network)
Northeast Petroleum University Reports Findings in Stroke (Ischemic stroke outco me prediction with diversity features from whole brain tissue using deep learnin g network)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Cerebrovascular Diseas es and Conditions - Stroke is the subject of a report. According to news reporti ng originating in Daqing, People’s Republic of China, by NewsRx journalists, res earch stated, “This study proposed an outcome prediction method to improve the a ccuracy and efficacy of ischemic stroke outcome prediction based on the diversit y of whole brain features, without using basic information about patients and im age features in lesions. In this study, we directly extracted dynamic radiomics features (DRFs) from dynamic susceptibility contrast perfusion-weighted imaging (DSCPWI) and further extracted static radiomics features (SRFs) and static enco ding features (SEFs) from the minimum intensity projection (MinIP) map, which wa s generated from the time dimension of DSC-PWI images.” The news reporters obtained a quote from the research from Northeast Petroleum U niversity, “After selecting whole brain features F from the combinations of DRFs , SRFs, and SEFs by the Lasso algorithm, various machine and deep learning model s were used to evaluate the role of F in predicting stroke outcomes. The experim ental results show that the feature F generated from DRFs, SRFs, and SEFs (Resne t 18) outperformed other single and combination features and achieved the best m ean score of 0.971 both on machine learning models and deep learning models and the 95% CI were (0.703, 0.877) and (0.92, 0.983), respectively. Be sides, the deep learning models generally performed better than the machine lear ning models.”
DaqingPeople’s Republic of ChinaAsiaCerebrovascular Diseases and ConditionsCyborgsEmerging TechnologiesHealt h and MedicineMachine LearningStroke
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