ArticleNeuroImage. Clinical2019
Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 4 of them syntheses that pooled it.
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Who cites it
65 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2023Pooled it
- Diagnostic performance of neuromelanin-sensitive magnetic resonance imaging for patients with Parkinson's disease and factor analysis for its heterogeneity: a systematic review and meta-analysis.European radiology · 2021Pooled it
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A Meta-Analysis.Journal of medical systems · 2020Pooled it
- Semi-supervised ensemble learning with interval type-2 fuzzy-rough sets for Parkinson's disease prediction from multi-omics.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- Radiomic Signature of the Substantia Nigra on Neuromelanin-Sensitive MRI Distinguishes Bipolar II Disorder From Unipolar Depression.Brain and behavior · 2026Article
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- Prediction of Motor Symptom Progression of Parkinson's Disease Through Multimodal Imaging-Based Machine Learning.Journal of imaging informatics in medicine · 2026Article
- Hybrid deep learning novel framework for classification of parkinson's disease.Scientific reports · 2026Article
- Novel applications of machine learning and computational neuroscience models to neuroimaging in Parkinson's disease and related disorders.Frontiers in aging neuroscience · 2026Review
- Inverse Association of Locus Coeruleus MRI Integrity With Structural Volume and Its Impact on Individual's Inattentiveness.Human brain mapping · 2025Article
- NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.Scientific reports · 2025Article
- AlzFormer: Video-based space-time attention model for early diagnosis of Alzheimer's disease.Neuroscience · 2025Article
- An Explainable Approach to Parkinson's Diagnosis Using the Contrastive Explanation Method-CEM.Diagnostics (Basel, Switzerland) · 2025Article
- An Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism.Journal of imaging informatics in medicine · 2025Article
- Explainable artificial intelligence to diagnose early Parkinson's disease via voice analysis.Scientific reports · 2025Article
- Using resting-state functional magnetic resonance imaging and contrastive learning to explore changes in the Parkinson's disease brain network and correlations with gait impairment.Quantitative imaging in medicine and surgery · 2025Article
- The Impact of the Blood-Brain Barrier and Its Dysfunction in Parkinson's Disease: Contributions to Pathogenesis and Progression.ACS omega · 2024Review
- Preliminary study of substantia nigra analysis by tensorial feature extraction.International journal of computer assisted radiology and surgery · 2024Article
5 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) as PD is characterized by loss of dopaminergic neurons in the SNc. Current techniques employ estimation of contrast ratios of the SNc, visualized on NMS-MRI, to discern PD patients from the healthy controls. However, the extraction of these features is time-consuming and laborious and moreover provides lower prediction accuracies. Furthermore, these do not account for patterns of subtle changes in PD in the SNc. To mitigate this, our work establishes a computer-based analysis technique that uses convolutional neural networks (CNNs) to create prognostic and diagnostic biomarkers of PD from NMS-MRI. Our technique not only performs with a superior testing accuracy (80%) as compared to contrast ratio-based classification (56.5% testing accuracy) and radiomics classifier (60.3% testing accuracy), but also supports discriminating PD from atypical parkinsonian syndromes (85.7% test accuracy). Moreover, it has the capability to locate the most discriminative regions on the neuromelanin contrast images. These discriminative activations demonstrate that the left SNc plays a key role in the classification in comparison to the right SNc, and are in agreement with the concept of asymmetry in PD. Overall, the proposed technique has the potential to support radiological diagnosis of PD while facilitating deeper understanding into the abnormalities in SNc.
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