ArticleBiomedicines2020
Using Artificial Neural Network to Discriminate Parkinson's Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images.
Article in Biomedicines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 42 citations in OpenAlex.
- Artificial Intelligence Algorithm-Based Ultrasound Image Segmentation Technology in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis.Journal of healthcare engineering · 2021Trial
- HILIC-MS/MS-based strategy for quantitative amino acid profiling in multiple biological matrices and its application in kidney disease samples.Analytical and bioanalytical chemistry · 2026Article
- Microglial tunneling nanotubes: an intercellular transfer facilitating mitochondrial dysfunction and neuroinflammation in experimental cerebral malaria.Apoptosis : an international journal on programmed cell death · 2026Article
- Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction.Journal of imaging informatics in medicine · 2025Article
- Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.Annals of nuclear medicine · 2025Article
- Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Article
- Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.European journal of nuclear medicine and molecular imaging · 2025Article
- Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions.Nature communications · 2024Review
- An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.BMC medical imaging · 2024Article
- A survey of detection of Parkinson's disease using artificial intelligence models with multiple modalities and various data preprocessing techniques.Journal of education and health promotion · 2024Review
- An Improved Method for Diagnosis of Parkinson's Disease using Deep Learning Models Enhanced with Metaheuristic Algorithm.Research square · 2023Article
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Explainable machine learning with pairwise interactions for the classification of Parkinson's disease and SWEDD from clinical and imaging features.Brain imaging and behavior · 2022Article
- Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks.Biomedicines · 2022Article
- Biomarkers in Neurodegenerative Diseases.Biomedicines · 2022Article
- Soft Attention Based DenseNet Model for Parkinson's Disease Classification Using SPECT Images.Frontiers in aging neuroscience · 2022Article
- Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).Sensors (Basel, Switzerland) · 2021Review
- Will Artificial Intelligence Outperform the Clinical Neurologist in the Near Future? Yes.Movement disorders clinical practice · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe 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).
methodsAbnormal 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.
resultsThe 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.
conclusionsThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.