Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images
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). Me...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
|
| Series: | Biomedicines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9059/9/1/12 |
| _version_ | 1797543556524539904 |
|---|---|
| author | Chung-Yao Chien Szu-Wei Hsu Tsung-Lin Lee Pi-Shan Sung Chou-Ching Lin |
| author_facet | Chung-Yao Chien Szu-Wei Hsu Tsung-Lin Lee Pi-Shan Sung Chou-Ching Lin |
| author_sort | Chung-Yao Chien |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:47:17Z |
| format | Article |
| id | doaj.art-424791556d5c428bac119a44c7b4ecd2 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-10T13:47:17Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-424791556d5c428bac119a44c7b4ecd22023-11-21T02:27:56ZengMDPI AGBiomedicines2227-90592020-12-01911210.3390/biomedicines9010012Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT ImagesChung-Yao Chien0Szu-Wei Hsu1Tsung-Lin Lee2Pi-Shan Sung3Chou-Ching Lin4Department of Biomedical Engineering, National Cheng Kung University, Tainan 704, TaiwanDepartment of Nuclear Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, TaiwanDepartment of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, TaiwanDepartment of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, TaiwanDepartment of Biomedical Engineering, National Cheng Kung University, Tainan 704, TaiwanBackground: 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.https://www.mdpi.com/2227-9059/9/1/12artificial neural networkdeep learningParkinson’s diseaseatypical parkinsonian syndromedopamine transporter SPECT |
| spellingShingle | Chung-Yao Chien Szu-Wei Hsu Tsung-Lin Lee Pi-Shan Sung Chou-Ching Lin Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images Biomedicines artificial neural network deep learning Parkinson’s disease atypical parkinsonian syndrome dopamine transporter SPECT |
| title | Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images |
| title_full | Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images |
| title_fullStr | Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images |
| title_full_unstemmed | Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images |
| title_short | Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images |
| title_sort | using artificial neural network to discriminate parkinson s disease from other parkinsonisms by focusing on putamen of dopamine transporter spect images |
| topic | artificial neural network deep learning Parkinson’s disease atypical parkinsonian syndrome dopamine transporter SPECT |
| url | https://www.mdpi.com/2227-9059/9/1/12 |
| work_keys_str_mv | AT chungyaochien usingartificialneuralnetworktodiscriminateparkinsonsdiseasefromotherparkinsonismsbyfocusingonputamenofdopaminetransporterspectimages AT szuweihsu usingartificialneuralnetworktodiscriminateparkinsonsdiseasefromotherparkinsonismsbyfocusingonputamenofdopaminetransporterspectimages AT tsunglinlee usingartificialneuralnetworktodiscriminateparkinsonsdiseasefromotherparkinsonismsbyfocusingonputamenofdopaminetransporterspectimages AT pishansung usingartificialneuralnetworktodiscriminateparkinsonsdiseasefromotherparkinsonismsbyfocusingonputamenofdopaminetransporterspectimages AT chouchinglin usingartificialneuralnetworktodiscriminateparkinsonsdiseasefromotherparkinsonismsbyfocusingonputamenofdopaminetransporterspectimages |