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Sensors 2019, 19(7), 1740; https://doi.org/10.3390/s19071740

Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging

1
Department of Information Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung City 84001, Taiwan
2
Department of Chinese Medicine, E-Da Cancer Hospital, No.1, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung City 82445, Taiwan
3
School of Chinese Medicine for Post-Baccalaureate, I-Shou University, No.8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung City 82445, Taiwan
4
Department of Medical Imaging and Radiological Science, I-Shou University, No.8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung City 82445, Taiwan
5
Department of Nuclear Medicine, E-Da Hospital, I-Shou University, No.1, Yida Rd, Jiaosu Village, Yanchao District, Kaohsiung City 82445, Taiwan
*
Author to whom correspondence should be addressed.
Received: 6 March 2019 / Revised: 8 April 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
(This article belongs to the Section Biosensors)
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Abstract

The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson’s disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky’s skewness coefficient, Pearson’s median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model. View Full-Text
Keywords: 99mTc-TRODAT-1; Parkinson’s disease; support vector machine; logistic regression 99mTc-TRODAT-1; Parkinson’s disease; support vector machine; logistic regression
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Hsu, S.-Y.; Lin, H.-C.; Chen, T.-B.; Du, W.-C.; Hsu, Y.-H.; Wu, Y.-C.; Tu, P.-W.; Huang, Y.-H.; Chen, H.-Y. Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging. Sensors 2019, 19, 1740.

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