Using artificial neural network to discriminate parkinson’s disease from other parkinsonisms by focusing on putamen of dopamine transporter SPECT images

Chung Yao Chien, Szu Wei Hsu, Tsung Lin Lee, Pi Shan Sung, Chou Ching Lin

研究成果: Article同行評審

摘要

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

原文English
文章編號12
頁(從 - 到)1-11
頁數11
期刊Biomedicines
9
發行號1
DOIs
出版狀態Published - 2021 十二月

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology(all)

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