ArticleEuropean journal of nuclear medicine and molecular imaging2018
Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson's disease based on [
Article in European journal of nuclear medicine and molecular imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 79 citations in OpenAlex.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Novel applications of machine learning and computational neuroscience models to neuroimaging in Parkinson's disease and related disorders.Frontiers in aging neuroscience · 2026Review
- Allometry correction in semi-quantitative analysis of dopamine transporter SPECT.EJNMMI research · 2025Article
- Striatal dopaminergic patterns and clinical features in frontotemporal dementia.Brain communications · 2025Article
- Postingestive reward acts through behavioral reinforcement and is conserved in obesity and after bariatric surgery.PLoS biology · 2024Article
- Relative sparing of dopaminergic terminals in the caudate nucleus is a feature of rest tremor in Parkinson's disease.NPJ Parkinson's disease · 2024Article
- Commentary: Application of automatic semi-quantification in clinical routine positron emission tomography brain studies is here to stay.European radiology · 2023Article
- Article
- Same same but different: dopamine transporter SPECT on scanners with CZT vs. NaI detectors.EJNMMI research · 2023Article
- Neurodegenerative diseases-Caps: a capsule network based early screening system for the classification of neurodegenerative diseases.Cognitive neurodynamics · 2022Article
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations.Diagnostics (Basel, Switzerland) · 2022Review
- Dopamine transporter single-photon emission computed tomography-derived radiomics signature for detecting Parkinson's disease.EJNMMI research · 2022Article
- Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes.European journal of nuclear medicine and molecular imaging · 2022Article
- Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease.NPJ Parkinson's disease · 2022Review
- Soft Attention Based DenseNet Model for Parkinson's Disease Classification Using SPECT Images.Frontiers in aging neuroscience · 2022Article
- Self-normalized Classification of Parkinson's Disease DaTscan Images.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- Impact of age and sex correction on the diagnostic performance of dopamine transporter SPECT.European journal of nuclear medicine and molecular imaging · 2021Article
- Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review.Annals of translational medicine · 2021Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
purposeThis work aimed to assess the potential of a set of features extracted from [
methodsSeven features were computed from each brain hemisphere: five standard features related to uptake ratios on the striatum and two features related to the estimated volume and length of the striatal region with normal uptake. The features were tested on a dataset of 652 [
resultsCross-validation results based on SVM have shown that, individually, the features that generated the highest accuracies were the length of the striatal region (96.5%), the putaminal binding potential (95.4%) and the striatal binding potential (93.9%) with no statistically significant differences among them. The highest classification accuracy was obtained using all features simultaneously (accuracy 97.9%, sensitivity 98% and specificity 97.6%). Generally, slightly better results were obtained using the SVM with no statistically significant difference to the other classifiers for most of the features.
conclusionsThe length of the striatal region uptake is clinically useful and highly valuable to confirm dopaminergic degeneration "in vivo" as an aid to the diagnosis of Parkinson's disease. It compares fairly well to the standard uptake ratio-based features, reaching, at least, similar accuracies and is easier to obtain automatically. Thus, we propose its day to day clinical use, jointly with the uptake ratio-based features, in the computer-aided diagnosis of dopaminergic degeneration in Parkinson's disease.
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Registered trials
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.