Abstract

BackgroundAutomated 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.MethodsKinect 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.ResultsThe 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.ConclusionsWe 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.

Highlights

  • Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD)

  • The 70 patients were divided into group of ‘0’, ‘1’, ‘2’, ‘3’ and ‘4’ respectively based on the results of MDS-UPDRS-III 3.13 score

  • A significant difference was found in age (p = 0.018), length of disease (p = 0.003), Hoehn-Yahr scale (p = 0.001), F1 (p = 0.010), F2 (p = 0.008), F3 (p < 0.001), F4 (p < 0.001), F5 (p < 0.001), F6 (p = 0.013), F7 (p < 0.001) and F8 (p = 0.008) among groups of different MDS-UPDRS-III 3.13 score

Read more

Summary

Introduction

Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD). Parkinson’s disease (PD) is the second most common chronic neurodegenerative disease after Alzheimer’s disease, characterized by motor impairments with tremor, rigidity and akinesia/bradykinesia as cardinal symptoms [1]. Postural abnormalities are frequent and disabling complications of PD. A cross-sectional study involving 811 PD patients showed the prevalence of postural. Common postural abnormalities in PD include sagittal abnormalities: camptocormia and anterocollis; coronal abnormalities: Pisa syndrome and scoliosis [3]. Abnormal postures cause pain and balance dysfunction, aggravating walking difficulties with important impacts on life quality [2, 4]. Recognition and standardized management will be helpful to delay the progression of postural abnormalities in PD to avoid worse outcomes

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call