Kinect-based objective assessment of the acute levodopa challenge test in parkinsonism: a feasibility study.

INTRODUCTION: The acute levodopa challenge test (ALCT) is an important and valuable examination but there are still some shortcomings with it. We aimed to objectively assess ALCT based on a depth camera and filter out the best indicators. METHODS: Fifty-nine individuals with parkinsonism completed ALCT and the improvement rate (IR, which indicates the change in value before and after levodopa administration) of the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was calculated. The kinematic features of the patients' movements in both the OFF and ON states were collected with an Azure Kinect depth camera. RESULTS: The IR of MDS-UPDRS III was significantly correlated with the IRs of many kinematic features for arising from a chair, pronation-supination movements of the hand, finger tapping, toe tapping, leg agility, and gait (rs = - 0.277 ~ - 0.672, P < 0.05). Moderate to high discriminative values were found in the selected features in identifying a clinically significant response to levodopa with sensitivity, specificity, and area under the curve (AUC) in the range of 50-100%, 47.22%-97.22%, and 0.673-0.915, respectively. The resulting classifier combining kinematic features of toe tapping showed an excellent performance with an AUC of 0.966 (95% CI = 0.922-1.000, P < 0.001). The optimal cut-off value was 21.24% with sensitivity and specificity of 94.44% and 87.18%, respectively. CONCLUSION: This study demonstrated the feasibility of measuring the effect of levodopa and objectively assessing ALCT based on kinematic data derived from an Azure Kinect-based system.

Evaluation of vaccination status of children with special health care needs in Suzhou, China, 2020-2022: A retrospective survey study.

Children with special health care needs (CSHCNs) are at an increased risk of vaccine-preventable infections (VPDs), but they also face the dilemma of vaccine hesitancy. We obtained information on pediatric visits from the Referral and Assessment Information System for Vaccination (RAISV) and information on vaccination from the Jiangsu Province Immunization Information System (JSIIS). We followed the occurrence of Adverse Events Following Immunization (AEFIs) and VPDs by actively calling and querying the China Information System for Disease Control and Prevention (CISDCP). The Poisson test was used to compare the incidence of AEFIs between groups. A total of 5,037 children who visited a vaccination assessment clinic were followed-up in this study. The majority were children with developmental anomalies (28.5%), certain conditions originating in the perinatal period (12.1%), and nervous system disorders (9.0%). Most CSHCNs (66.9%) were advised to have all vaccines according to routine practice, 29.0% were advised to have partial vaccination, and 4.1% were advised to delay all vaccines and wait for future assessment. A total of 201 (4.0%) CSHCNs were not vaccinated, although they were assessed to be eligible for vaccination. By querying the immunization planning module in CISDCP, we observed 55 AEFI cases, which amounted to an incidence rate of 1.2 per 1,000, and the occurrence of abnormal reactions was not significantly different compared with the general population. The vaccination program following the designed workflow for CSHCNs was safe and could be recommended in other areas.

Automated and accurate assessment for postural abnormalities in patients with Parkinson's disease based on Kinect and machine learning.

BACKGROUND: Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson's disease (PD). The combination of depth camera and machine learning makes this purpose possible. METHODS: Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors' MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale) scores. An objective function was implanted to further improve the human-machine consistency. RESULTS: The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine's and doctors' score was 0.940 (95%CI, 0.905-0.962), meaning the machine was highly consistent with the doctors' judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity. CONCLUSIONS: We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD.