ArticleFrontiers in aging neuroscience2022
Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 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
- 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
- Early classification of functional connectomes in Parkinson's disease: a comparison of machine learning classifiers using multi-scale topological features.BMC medical informatics and decision making · 2025Article
- Utilizing combined quantitative multiparametric MRI as potential biomarkers for improved early-stage parkinson's disease diagnosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Attention-based Transformer-LSTM architecture for early diagnosis and staging of early-stage Parkinson's disease using fNIRS data.Frontiers in aging neuroscience · 2025Article
- Multimodal MRI changes associated with non-motor symptoms of rapid eye movement sleep behaviour disorder in Parkinson's disease patients.Neuroradiology · 2025Article
- Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease.European radiology · 2024Article
- Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.Tomography (Ann Arbor, Mich.) · 2024Article
- Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data.Biosensors · 2024Article
- A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning.Frontiers in aging neuroscience · 2024Article
- Radiomics for Parkinson's disease classification using advanced texture-based biomarkers.MethodsX · 2023Article
- A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.Heliyon · 2023Article
- Identification of discriminative neuroimaging markers for patients on hemodialysis with insomnia: a fractional amplitude of low frequency fluctuation-based machine learning analysis.BMC psychiatry · 2023Article
- Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor.Frontiers in neurology · 2023Article
- Brain disease research based on functional magnetic resonance imaging data and machine learning: a review.Frontiers in neuroscience · 2023Review
- Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis.Heliyon · 2022Article
- Combined brain network topological metrics with machine learning algorithms to identify essential tremor.Frontiers in neuroscience · 2022Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently,
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