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.     

应用锥形束CT对种植区牙槽骨质量分类方法的改良研究

目的    应用锥形束CT（cone beam CT，CBCT）测量种植区牙槽骨质量并结合Lekholm和Zarb分类法对骨质量分类方法进行改良，以期为提高牙槽骨质量分类的准确性提供指导。方法    选择2015年10月至2017年10月在呼和浩特市口腔医院种植科进行种植修复的196例患者术前CBCT影像资料，应用 Invivo5诊断设计软件测量306个种植位点的牙槽骨皮质骨厚度和松质骨密度（皮质骨与松质骨的CT值差值），以Lekholm和Zarb分类法为基础结合测量数据对骨质量进行改良后的量化分类。结果    牙槽骨皮质骨厚度为0.18 ~ 2.89 mm，中位数为0.92 mm。皮质骨与松质骨的CT值差值为88.5 ~ 667.8 HU，应用百分位数法找到33.3%和66.6%对应的数值分别为297.8 HU和356.1 HU。依据此数据结合Lekholm和Zarb分类法将196例患者的306个种植位点骨质量分为4类，其中Ⅰ类骨种植位点46个（占15.0%），Ⅱ类骨104个（占34.0%），Ⅲ类骨114个（占37.3%），Ⅳ类骨42个（占13.7%）。结论    结合Lekholm和Zarb分类法，应用CBCT测量种植区牙槽骨质量并进行分类方法的改良，可为临床医生术前评估牙槽骨质量提供一定参考。     

Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care

Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.     

不同剂量骨水泥PKP联合雌激素对绝经后女性骨质疏松性腰椎体压缩性骨折患者的影响

目的探讨不同剂量骨水泥PKP联合雌激素对绝经后女性骨质疏松性腰椎体压缩性骨折患者的影响。方法选取2015年1月至2018年6月我院收治的120例老年女性骨质疏松性腰椎体压缩性骨折患者为研究对象,按行PKP手术治疗时骨水泥注入的剂量不同分为三组(A组2-4 mL、B组4-5 mL、C组5-6 mL),所有患者均随访1年,比较术后三组患者VAS评分、ODI评分、伤椎椎体前缘高度、椎体中部高度及并发症的发生率。结果三组患者相比,治疗前VAS评分、ODI评分组间比较差异无显著性(P0.05);术后3天及1年后随访时,三组患者各项指标均较治疗前显著改善,组内比较差异具有显著性(P0.05),组间比较差异无显著性(P0.05);治疗前伤椎椎体前缘高度及椎体中部高度组间比较差异无显著性(P0.05);术后3天及1年后随访时,三组患者治疗后椎体前缘、中间高度均较术前明显增加(P0.05),组间比较椎体前缘高度:C组B组A组,椎体中间高度:C组B组A组,且组间比较有统计学差异(P0.05); A组患者并发症的发生率(12.5%)明显低于B组(30.0%)、C组(41.0%),有统计学差异(P0.05)。结论 PKP术中注入小剂量骨水泥(2-4 mL)对于治疗老年女性OVCFs具有良好的治疗效果及较高的安全性。