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.     

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。     

“人工智能+X”背景下医学信息管理专业研究生培养模式

阐述当前“人工智能+X”背景下市场对医学信息管理专业人才能力的需求，分析医学信息管理专业人才培养现状，提出从重塑学科人才培养目标、优化课程内容与课程设置、建设“双师型”导师队伍、搭建多方协同共建共享在线平台及设立“政用产学研”联合培养基地等方面探索医学信息管理专业研究生培养模式，以期培养适应人工智能时代发展，具备学科优势特色的高层次、高水平、高质量的复合型、应用型、创新型人才。     

椎弓根螺钉三椎体内固定对脊椎爆裂性骨折患者椎体与神经功能状态的影响观察

目的:探讨椎弓根螺钉三椎体内固定对脊椎爆裂性骨折患者椎体与神经功能状态的影响.方法:选取2013年7月至2015年1月于本院进行治疗的58例脊椎爆裂性骨折患者为研究对象,对其进行回顾性研究,将其根据手术方式不同分为对照组(短节段椎弓根内固定组)29例和观察组(椎弓根螺钉三椎体内固定组)29例,然后将两组患者手术前与手术后不同时间的伤椎恢复指标与血清神经功能相关指标进行比较.结果:观察组手术后不同时间的伤椎恢复指标中的后凸Cobb角、椎体平移率、椎体高度丢失率与血清神经功能相关指标中的BDNF、NSE、NGF及S100B均显著好于同期的对照组指标,并且明显好于其治疗前,均有显著性差异(P＜0.05).结论:椎弓根螺钉三椎体内固定可更为有效地恢复脊椎爆裂性骨折患者椎体参数,并且更有助于神经功能状态的恢复.     

Primary epidural lumbar Ewing sarcoma presenting as cauda equina syndrome in an adult patient who underwent surgical decompression: A case report

Ewing sarcoma (ES) is a rare primary neoplasm in the lumbar adult spine and may mimic a benign tumor. In this case, after a patient's three-month history of lower back pain and rapidly progressing leg numbness and weakness, magnetic resonance imaging (MRI) showed a mass in the third lumbar vertebra. At a two-month follow-up, imaging showed a fracture, compression and lesion enlargement. Decompression and fixation confirmed ES, and the patient began combined radiotherapy and chemotherapy. Two months postoperatively, residual ES was suspected on MRI. The patient underwent a second surgery, and histopathology confirmed necrosis. A six-month follow-up after the first surgery showed no tumor recurrence. This case supports the inclusion of ES in the differential diagnosis of pathologic spinal fracture. Early decompression and spinal fixation are critical for preserving neurologic and spinal functions in ES complicated by a compression fracture. Combined adjuvant radiotherapy and chemotherapy remain the standard therapeutic strategy.