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.     

以就业为导向的脊柱相关疾病的推拿教学策略

目的培养脊柱及相关疾病推拿治疗的应用型人才。方法采用编写应用型教材、把教室转移到诊室和示教室、教材-案例“逼真”教学法、创造学生在临床真实环境下的动手操作机会和产-学-研相结合教学法等手段和方法,在内容改革上纳入目前医院和社会机构多使用的规划教材之外的手法技术、现代康复技术和世界各地的脊柱手法技术,拓宽知识面,与临床应用接轨;发挥考试的导向功能,通过改革考试和评价方法,切实培养应用型人才。结果教学效果良好,有效提升了学生临床适应能力和社会生存能力。结论以就业为导向的脊柱及相关疾病的推拿教学模式值得进一步探索。     

A comprehensive 3D CT based classification of intertrochanteric fracture

IntroductionDespite advancements in surgical techniques complications like implant failure is very common after the fixation of intertrochanteric fractures. Classifying these complex fractures based on plain radiographs underestimates the complexity of these fractures which in turn leads to complications. We propose a comprehensive classification of the intertrochanteric fractures based on 3D Non Contrast Computed Tomography (3D NCCT) scan.Material and methodsA total of 102 patients (51 males and 51 females) with intertrochanteric fractures were included in this study conducted over a time period of 22 months in a Tertiary care center in North India. NCCT proximal femur of the intertrochanteric fracture patients was done to formulate a new CT classification system and classify all fractures. Intra and inter-observer reliability was tested using kappa variance.ResultsNew classification system was proposed which included 3 main and a total of 6 groups. All the fractures were classifiable into the new system. Kappa variance of the study showed a good intra and interobserver reliability (0.95 and 0.90) proving clinical agreement of the classification.ConclusionThis new 3D-CT based classification has the advantages of being easy, comprehensible with high intra and inter-observer reliability. This 3DCT based classification can prove to be useful to detect occult intertrochanteric fractures undetectable in plain radiographs as well as choosing the optimum treatment plan.     

Prenatal Sonography in Suspected Proximal Gastrointestinal Obstructions: Diagnostic Accuracy and Neonatal Outcomes

BackgroundThe purpose of this study was to assess diagnostic accuracy and neonatal outcomes in fetuses with a suspected proximal gastrointestinal obstruction (GIO).MethodsAfter IRB approval, a retrospective chart review was conducted on prenatally suspected and/or postnatally confirmed cases of proximal GIO at a tertiary care facility (2012–2022). Maternal-fetal records were queried for presence of a double bubble ± polyhydramnios, and neonatal outcomes were assessed to calculate the diagnostic accuracy of fetal sonography.ResultsAmong 56 confirmed cases, the median birthweight and gestational age at birth were 2550 g [interquartile range (IQR) 2028–3012] and 37 weeks (IQR 34–38), respectively. There was one (2%) false-positive and three (6%) false-negatives by ultrasound. Double bubble had a sensitivity, specificity, positive predictive value, and negative predictive value for proximal GIO of 85%, 98%, 98%, and 83%, respectively. Pathologies included 49 (88%) with duodenal obstruction/annular pancreas, three (5%) with malrotation, and three (5%) with jejunal atresia. The median postoperative length of stay was 27 days (IQR 19–42). Cardiac anomalies were associated with significantly higher complications (45% vs 17%, p = 0.030).ConclusionsIn this contemporary series, fetal sonography has high diagnostic accuracy for detecting proximal gastrointestinal obstruction. These data are informative for pediatric surgeons in prenatal counseling and preoperative discussions with families.Level of EvidenceDiagnostic Study, Level III.