Utility of Tissue Similarity Maps Based on Relative Cerebral Blood Volume for Grading Gliomas as Validated by Histological Results

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Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

铸造支架联合NITI悬臂梁矫正舌倾下颌磨牙的临床效果评价

目的 评价NITI悬臂梁在矫正舌倾下颌磨牙中的临床效果。方法 选择16例单侧下颌第二磨牙舌倾的病例为研究对象,带垫铸造支架连接双侧下颌后牙,提供颌内支抗、解除咬合锁结,0.018英寸×0.025英寸或0.019英寸×0.025英寸NITI悬臂梁提供颊向旋转力矩和压低力。采用Graphpad Prism 6.0 软件对治疗前、后所测数据进行配对 t 检验。结果 所有患牙均获得直立,牙轴变化24°±1.2°（P<0.01）,近中舌尖到正中矢状面垂直距离变化（3±0.8） mm（P<0.05）,牙周状况良好,咬合关系稳定。结论 铸造支架联合NITI 悬臂梁可提供有效力学机制,矫正舌倾下颌磨牙。     

基于肿瘤影像学异质性分析CT在诊断十二指肠恶性肿瘤的价值

目的 探讨十二指肠肿瘤血流量存在的异质性及容积灌注CT成像对降低肿瘤异质性的影响。方法 纳入我院2013年1月至2014年11月十二指肠结节病例的132例,其中腺癌48例,间质瘤52例,淋巴瘤32例。所有的病例均行单层面灌注CT和容积灌注CT成像扫描,比较两者对十二指肠恶性肿瘤的诊断效能,采用变异系数评价两者在影像学异质性的差异,评价肿瘤影像学异质性对十二指肠恶性肿瘤的影响。结果 容积灌注CT成像与单层面灌注CT诊断恶性结节的效能其敏感性、特异性、准确性、阴性预测值、阳性预测值比较均无明显差别(P0.05);中高分化组及低分化组两个亚组的容积灌注CT成像的变异系数也均小于单层面灌注CT的变异系数,组间相比具有统计学差异(P0.05);血流量差异性的大小相比具有统计学差异(P0.05)。结论 容积灌注CT成像与单层面灌注CT检查在诊断效能上无明显差异,稳定性好,肿瘤影像学异质性对评价十二指肠肿瘤血流量有着一定影响,容积灌注CT成像能够降低肿瘤异质性的影响。     

体视学法在颅内血肿体积测量的应用

目的介绍改进后的体视学法测量颅内血肿体积,并验证其准确性。方法在92例颅内出血的CT图像上,同时运用体视法、CT定量法测量颅内血肿体积,以CT定量法的结果为标准验证体视法的准确性。结果按血肿体积大小分为5组,各组CT定量法、体视学法所测得体积采用配对样本的t检验分析P值皆大于0.05,即CT定量法、体视学法所测血肿体积无统计学差异。结论体视学法测量颅内血肿准确可靠,在CT工作站对颅内血肿病例行胶片打印排版时,在图像上加上合适的网格,可方便临床医生快速运用体视法准确测量颅内血肿体积。     

采用半透明效果实现医学三维整体可视化

目的通过改进直接体绘制技术（DVR）算法中的关键步骤，绘制能表达数据场中不同层次组织的内部结构。方法根据界面对光线的反射和物质本身对光线的衰减，将数据场划分为边界不丰富的简单数据场和边界丰富的复杂数据场，从而采用分段线性函数来构造阻光度传递函数（TF）；将数据场中低灰度体素点看为一种不发光但会导致采样光线衰减的物质；根据采样结束时采样光线的阻光度分布来调整TF，完成三维绘制。结果绘制出了不同层次组织的内部结构。结论本算法有效地解决了体素点相互遮挡的问题，实现了医学三维整体可视化。     

Proposed new bladder volume monitoring device based on impedance measurement

A new implantable bladder volume-monitoring device based on the impedance measurement of the detrusor muscle is described. The system is completely autonomous and forms a mixed-signal (analogue/digital) feedback loop with a neuro-stimulator to rectify bladder dysfunctions (incontinence and retention) through neuromuscular stimulation techniques. A programmable instrumentation amplifier and a signal processing block, to eliminate the artefacts caused by the patient’s movements, have been designed and tested. The layout for the signal processing block has been realised in 0.8 μm BiCMOS technology.