ArticleDiagnostics (Basel, Switzerland)2020
Detection of Parkinson's Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 145 citations in OpenAlex.
- A survey of deep learning techniques in detecting neurological disorders using MRI.Biomedical engineering online · 2026Pooled it
- 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
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Morphometric Explanations for Deep Learning Based Neuroimaging Classifiers.Human brain mapping · 2026Article
- Novel applications of machine learning and computational neuroscience models to neuroimaging in Parkinson's disease and related disorders.Frontiers in aging neuroscience · 2026Review
- Explainable 3D CNNs link regional and network level disruption in early Parkinson's MRIs to symptom progression.bioRxiv : the preprint server for biology · 2025Article
- Multi-modal deep learning framework for early detection of Parkinson's disease using neurological and physiological data for high-fidelity diagnosis.Scientific reports · 2025Article
- A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI.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
- An efficient ranking-based ensembled multiclassifier for neurodegenerative diseases classification using deep learning.Journal of neural transmission (Vienna, Austria : 1996) · 2025Article
- Applications of machine learning for computer-aided diagnosis of Parkinson's disease: progress and benchmark case study.Artificial intelligence review · 2025Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- Automatic Transcranial Sonography-Based Classification of Parkinson's Disease Using a Novel Dual-Channel CNXV2-DANet.Bioengineering (Basel, Switzerland) · 2024Article
- An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.BMC medical imaging · 2024Article
- Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.NPJ Parkinson's disease · 2024Article
- Radiomics for Parkinson's disease classification using advanced texture-based biomarkers.MethodsX · 2023Article
- A review of emergent intelligent systems for the detection of Parkinson's disease.Biomedical engineering letters · 2023Review
- An Improved Method for Diagnosis of Parkinson's Disease using Deep Learning Models Enhanced with Metaheuristic Algorithm.Research square · 2023Article
- Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.Physical and engineering sciences in medicine · 2023Article
- Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson's Disease in the First 6 Years of Levodopa Treatment.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Parkinson's Disease is a neurodegenerative disease that affects the aging population and is caused by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). With the onset of the disease, the patients suffer from mobility disorders such as tremors, bradykinesia, impairment of posture and balance, etc., and it progressively worsens in the due course of time. Additionally, as there is an exponential growth of the aging population in the world the number of people suffering from Parkinson's Disease is increasing and it levies a huge economic burden on governments. However, until now no therapeutic method has been discovered for completely eradicating the disease from a person's body after it's onset. Therefore, the early detection of Parkinson's Disease is of paramount importance to tackle the progressive loss of dopaminergic neurons in patients to serve them with a better life. In this study, 3T T1-weighted MRI scans were acquired from the Parkinson's Progression Markers Initiative (PPMI) database of 406 subjects from baseline visit, where 203 were healthy and 203 were suffering from Parkinson's Disease. Following data pre-processing, a 3D convolutional neural network (CNN) architecture was developed for learning the intricate patterns in the Magnetic Resonance Imaging (MRI) scans for the detection of Parkinson's Disease. In the end, it was observed that the developed 3D CNN model performed superiorly by completely aligning with the hypothesis of the study and plotted an overall accuracy of 95.29%, average recall of 0.943, average precision of 0.927, average specificity of 0.9430, f1-score of 0.936, and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) score of 0.98 for both the classes respectively.
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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.