在FDR 5%水平下,CMR性状对44个性状和疾病具有显著的预测能力,其中包括循环系统疾病(ICD-10组代码:“I”);内分泌、营养和代谢疾病(ICD-10: "E");内分泌、营养和代谢疾病(ICD-10: "F");心理健康与认知特征;心血管疾病(自我报告)、生物标志物和危险因素;与疾病家族史(预测相关范围= (0.028,0.5),P范围= (...
在FDR 5%水平下,CMR性状对44个性状和疾病具有显著的预测能力,其中包括循环系统疾病(ICD-10组代码:“I”);内分泌、营养和代谢疾病(ICD-10: "E");内分泌、营养和代谢疾病(ICD-10: "F");心理健康与认知特征;心血管疾病(自我报告)、生物标志物和危险因素;与疾病家族史(预测相关范围= (0.028,0.5),P范围= (...
performed, with more than 400 patterns of testing. Magnetic resonance imaging of the brain or spine was ordered in 23.2 % of patients, whereas a glucose tolerance test was rarely obtained (1.0 %). Mean Medicare expenditures were significantly higher in the diagnostic period than in the baseline ...
As structural brain abnormalities have been reported in infantile autism, the aim of this study was to determine whether such findings also exist in Asperger Syndrome (AS). The diagnosis of Asperger Syndrome was based on the criteria in ICD-10 and DSM-IV. Brain magnetic resonance imaging (MRI...
Secondary end points included cardiovascular mortality or cardiac transplantation; an arrhythmic composite of SCD or aborted SCD (appropriate ICD shock, non-fatal ventricular fibrillation, or sustained ventricular tachycardia); and a composite of heart failure (HF) death, HF hospitalization, or cardiac ...
The structural images of the brain were acquired on the same 3.0 T GE Signa equipped with an eight channel phased array head coil at Hefei Fourth People’ Hospital. The T1-weighted MRI was scanned with the following parameters: repetition time = 8.5 ms; echo time = 3.2 ms; invers...
Food and Drug Administration (FDA) approval of its ImageReady™ MRI labeling for the Vercise Gevia™ Deep Brain Stimulation (DBS) System to be used in a full-body MRI (1.5 Tesla MRI conditional when all conditions of use are met). This system, with the Vercise Cartesia™ Directional...
如MRI 扫描过程中,废弃的引线端处可能会比正常工作时累积更多的热量,从而灼伤心脏组织。患者携带心脏起搏器或ICD 误入磁场后,设备功能可能会发生改变[9],要立即将患者撤离MRI 检查室并联系患者的主管医师,仔细询问、观测患者情况,同时认真检查心脏植入设备的运行状况。
We found that the MRI-based subtypes predicted CDP whilst single MRI variables (lesion load and whole brain volume) did not, suggesting that a comprehensive model is necessary to achieve the difficult task to predict disability progression. We used data from a large number of clinical trials and...
Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict “healthy” brain age. Brain regions most informative for the prediction includ...