Abstract Gait impairments are among the most common and disabling symptoms of Parkinson’s disease and worsen as the disease progresses. Early detection and diagnosis of subtype-specific gait deficits, as well as progression monitoring, can help to implement effective and preventive personalized treatment for PD patients. Yet, the gait features have not been fully studied in PD and its motor subtypes. To characterize comprehensive and objective gait alterations and to identify the potential gait biomarkers for early diagnosis, subtype differentiation, and disease severity monitoring. We analyzed gait parameters related to upper/lower limbs, trunk and lumbar, and postural transitions from 24 tremor-dominant (TD) and 20 postural instability gait difficulty (PIGD) dominant PD patients who were in early stage and 39 matched healthy controls (HC) during the Timed Up and Go test using wearable sensors. Results show: (1) Both TD and PIGD groups showed restricted backswing range in bilateral lower extremities and more affected side (MAS) arm, reduced trunk and lumbar rotation range in the coronal plane, and low turning efficiency. The receiver operating characteristic (ROC) analysis revealed these objective gait features had high discriminative value in distinguishing both PD subtypes from the HC with the area under the curve (AUC) values of 0.7~0.9 ( p &lt; 0.01). (2) Subtle but measurable gait differences existed between TD and PIGD patients before the onset of clinically apparent gait impairment. (3) Specific gait parameters were significantly associated with disease severity in TD and PIGD subtypes. Objective gait biomarkers based on wearable sensors may facilitate timely and personalized gait treatments in PD subtypes through early diagnosis, subtype differentiation, and disease severity monitoring. 

pdf/wearable-sensor-based-quantitative-gait-analysis-in-531dqby25y.pdf

Wearable sensor-based quantitative gait analysis in Parkinson’s disease patients with different motor subtypes

The heterogeneity of Parkinson's disease (PD) presents considerable challenges for accurate diagnosis, particularly during early-stage disease, when the symptoms may be extremely subtle. This study aimed to assess the accuracy of a convolutional neural network (CNN) technique based on the 6-min walk test (6MWT) measured using wearable sensors to distinguish patients with early-stage PD (n = 78) from healthy controls (n = 50). The participants wore six sensors, and performed the 6MWT. The time-series data were converted into new images. The results revealed that the gyroscopic vertical component of the lumbar spine displayed the highest classification accuracy of 83.5%, followed by those of the thoracic spine (83.1%) and right thigh (79.5%) segment. These findings suggest that the 6MWT and CNN models may facilitate earlier diagnosis and monitoring of PD symptoms, enabling clinicians to provide timely treatment during the critical transition from normal to pathologic gait patterns. 

Convolutional neural network based detection of early stage Parkinson’s disease using the six minute walk test

Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.

<title>Abstract</title> Digital technologies for monitoring motor symptoms of Parkinson’s Disease (PD) have underwent a strong evolution during the past years. Although it has been shown for several devices that derived digital gait features can reliably discriminate between healthy controls and people with PD, the specific gait tasks best suited for monitoring motor symptoms and especially their progression, remain unclear. Furthermore, the potential benefit as endpoint in a clinical trial context has not been investigated so far. In this study we employed a digital gait device manufactured by Portabiles HCT, which has been used by 339 patients within the LuxPark cohort (n = 161, Luxembourg) as well as within routine clinical care visits at the University Medical Center Erlangen (n = 178, Erlangen, Germany). Linear (mixed) models were used to assess the association of task-specific digital gait features with disease progression and motor symptom severity measured by several clinical scores. Furthermore, we employed machine learning to evaluate whether digital gait assessments were prognostic for patient-level motor symptom progression. Overall, digital gait features derived from Portabiles digital gait device were found to effectively monitor motor symptoms and their longitudinal progression. At the same time the prognostic performance of digital gait features was limited. However, we could show a strong reduction in required sample size, if digital gait features were employed as surrogates for traditional endpoints in a clinical trial context. Thus, Portabiles digital gait device provides an effective way to objectively monitor motor symptoms and their progression in PD. Furthermore, the digital gait device bears strong potential as an alternative and easily assessable endpoint predictor in a clinical trial context. 

Objective Monitoring of Motor Symptom Severity and their Progression in Parkinson’s Disease Using a Digital Gait Device

Deep Belief Bayesian Joint Conditional Detection of coronary artery plaque and stenosis in X-ray angiography images

Gait impairment leads to reduced social activities and low quality of life in people with Parkinson's disease (PD). PD is associated with unique gait signs and distributions of gait features. The assessment of gait characteristics is crucial in the diagnosis and treatment of PD. At present, the number and distribution of gait features associated with different PD stages are not clear. Here, we used whole-body multinode wearable devices combined with machine learning to build a classification model of early PD (EPD) and mild PD (MPD). Our model exhibited significantly improved accuracy for the EPD and MPD groups compared with the healthy control (HC) group (EPD vs. HC accuracy=0.88, kappa=0.75, AUC=0.88; MPD vs. HC accuracy=0.94, kappa=0.84, AUC =0.90). Furthermore, the distribution of gait features was distinguishable among the HC, EPD and MPD groups (EPD based on variability features (40%); MPD based on amplitude features (30%)). Here, we showed promising gait models for PD classification and provided reliable gait features for distinguishing different PD stages. Further multicentre clinical studies are needed to generalize the findings.

Specific Distribution of Digital Gait Biomarkers in Parkinson's disease Using Body-Worn Sensors and Machine Learning.

This research aimed at discussing the diagnosis effect of coronary artery magnetic resonance angiography (MRA) combined with computed tomography (CT) angiography (CTA) based on the back-projection filter reconstruction (BPFR) algorithm in coronary heart disease (CHD), and its role in the diagnosis of coronary artery disease (CAD). Sixty patients with CHD were selected and randomly rolled into group A (undergone MRA examination), group B (undergone CTA examination), and group C (undergone MRA + CTA), with 20 cases in each group. Taking the diagnostic results of coronary angiography as the gold standard, the MRA and CTA images were reconstructed using a BPFR algorithm, and a filter function was added to solve the problem of image sharpness. In addition, the iterative reconstruction algorithm and the Fourier transform analysis method were introduced. As a result, the image clarity and resolution obtained by the BPFR algorithm were better than those obtained by the Fourier transform analytical method and the iterative reconstruction algorithm. The accuracy of group C for the diagnosis of mild coronary stenosis, moderate stenosis, and severe stenosis was 94.02%, 96.13%, and 98.01%, respectively, which was significantly higher than that of group B (87.5%, 90.2%, and 88.4%) and group C (83.4%, 89.1%, and 91.5%) (P < 0.05). The sensitivity and specificity for the diagnosis of noncalcified plaque in group C were 87.9% and 89.2%, respectively, and the sensitivity and specificity for the diagnosis of calcified plaque were 84.5% and 78.4%, respectively, which were significantly higher than those in groups B and C (P < 0.05). In summary, the BPFR algorithm had good denoising and artifact removal effects on coronary MRA and CTA images. The combined detection of reconstructed MRA and CTA images had a high diagnostic value for CHD.

/pdf/coronary-artery-magnetic-resonance-angiography-combined-with-10tqb7e6.pdf

Coronary Artery Magnetic Resonance Angiography Combined with Computed Tomography Angiography in Diagnosis of Coronary Heart Disease by Reconstruction Algorithm

Objective We aimed to compare the motor effect of bilateral globus pallidus interna (GPi) deep brain stimulation (DBS) on motor subtypes of Parkinson’s disease (PD) patients and identify preoperative predictive factors of short-term motor outcome. Methods We retrospectively investigated bilateral GPi DBS clinical outcomes in 55 PD patients in 1 year follow up. Motor outcome was measured by the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III before and 1 year after surgery. Clinical outcomes were compared among different motor subtypes. Preoperative predictors of motor outcome were assessed by performing univariate and multivariate linear regression and logistic regression analyses. Results At 1 year following implantation, GPi DBS significantly improved the off-medication MDS-UPDRS III scores in all motor subtype cohorts, with prominent improvement in tremor. No significant difference of postoperative motor symptoms changes was found except greater tremor improvement achieved in both the tremor-dominant (TD) and indeterminate (IND) patients compared to the postural instability and gait difficulty (PIGD) patients. High percentage of PIGD patients were weak responders to DBS. Better levodopa responsiveness and more severe tremor predicted greater overall improvement of motor function in the entire cohort. Similarly, both levodopa responsiveness and tremor improvement were confirmed as predictors for motor improvement in PIGD patients. Conclusion Bilateral GPi DBS could effectively improve motor outcomes in PD patients regardless of motor subtypes. Both TD and IND patients obtained larger tremor improvement. The intensity of levodopa responsiveness and the severity of tremor could serve as predictors of motor improvement 1 year after GPi DBS.

Bilateral globus pallidus interna deep brain stimulation in Parkinson’s disease: Therapeutic effects and motor outcomes prediction in a short-term follow up

Minimally invasive direct coronary artery bypass (MIDCAB) with the left internal thoracic artery (LITA) provides excellent long‐term results for isolated left anterior descending coronary artery (LAD) disease. We tried to evaluate the impact of the previous percutaneous coronary intervention (p‐PCI) for LAD on the clinical outcomes of MIDCAB in this study.

Minimally invasive direct coronary artery bypass after percutaneous coronary intervention

When Diagnosis Is Made By Playing 'Shuffled Cards': A Non-Invasive, Real-Time, Adaptive Method to Functionally Evaluate Coronary Artery Based on Coronary Angiography

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