根据急性阶段非对比CT和临床信息预测缺血性中风的功能结果

Yongkai Liu1, Yannan Yu1, Jiahong Ouyang1

  • 1From the Departments of Radiology (Y.L., J.O., B.J., S.O., Y. Yang, M.E.M., J.J.H., G.Z.) and Neurology (M.L., G.A.), Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305-5488; Department of Radiology, University of California-San Francisco, San Francisco, Calif (Y. Yu); Department of Electrical Engineering (J.O.) and Department of Environmental Health and Safety (J.W.), Stanford University, Stanford, Calif; Henry M. Gunn Senior High School, Palo Alto, Calif (S.L.L.); National Heart and Lung Institute, Imperial College London, London, UK (G.Y.); Neurology Service, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Switzerland (P.M.); Department of Neurology, University of California Los Angeles, Los Angeles, Calif (D.S.L.); and Department of Neuroradiology, University of Texas MD Anderson Cancer Center, Houston, Tex (M.W.).

Radiology
|October 15, 2024
PubMed
概括

结合CT扫描和临床数据的深度学习模型准确地预测了缺血性中风患者的90天结果. 这种融合方法提高了与仅使用成像或临床数据相比的预测准确性.