Early Diagnosis of Parkinsons Disease by Analyzing Magnetic Resonance Imaging Brain Scans and Patient Characteristics

• URL: http://arxiv.org/abs/2201.04631v1
• Date: Wed, 12 Jan 2022 15:51:54 GMT
• Title: Early Diagnosis of Parkinsons Disease by Analyzing Magnetic Resonance Imaging Brain Scans and Patient Characteristics
• Authors: Sabrina Zhu
• Abstract summary: Parkinsons disease, PD, is a chronic condition that affects motor skills and includes symptoms like tremors and rigidity. This research proposes to accurately diagnose PD severity with deep learning by combining symptoms data and MRI data from the Parkinson Progressions Markers Initiative database. The symptoms based model integrates a fully connected deep learning neural network, and the MRI scans and hybrid models integrate transfer learning based convolutional neural networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parkinsons disease, PD, is a chronic condition that affects motor skills and includes symptoms like tremors and rigidity. The current diagnostic procedure uses patient assessments to evaluate symptoms and sometimes a magnetic resonance imaging or MRI scan. However, symptom variations cause inaccurate assessments, and the analysis of MRI scans requires experienced specialists. This research proposes to accurately diagnose PD severity with deep learning by combining symptoms data and MRI data from the Parkinsons Progression Markers Initiative database. A new hybrid model architecture was implemented to fully utilize both forms of clinical data, and models based on only symptoms and only MRI scans were also developed. The symptoms based model integrates a fully connected deep learning neural network, and the MRI scans and hybrid models integrate transfer learning based convolutional neural networks. Instead of performing only binary classification, all models diagnose patients into five severity categories, with stage zero representing healthy patients and stages four and five representing patients with PD. The symptoms only, MRI scans only, and hybrid models achieved accuracies of 0.77, 0.68, and 0.94, respectively. The hybrid model also had high precision and recall scores of 0.94 and 0.95. Real clinical cases confirm the strong performance of the hybrid, where patients were classified incorrectly with both other models but correctly by the hybrid. It is also consistent across the five severity stages, indicating accurate early detection. This is the first report to combine symptoms data and MRI scans with a machine learning approach on such a large scale.

Related papers

• Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators [72.79532467687427]
  Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled and compressed measurements. Deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Early diagnosis of Alzheimer's disease from MRI images with deep learning model [0.7673339435080445]
  Alzheimer's disease is the most common cause of dementia worldwide. classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI) In this article, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images.
  arXiv  Detail & Related papers  (2024-09-27T15:07:26Z)
• Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI [3.639043225506316]
  We introduce recon-all-clinical, a novel method for cortical reconstruction, registration, parcellation, and thickness estimation in brain MRI scans. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions. We tested recon-all-clinical on multiple datasets, including over 19,000 clinical scans.
  arXiv  Detail & Related papers  (2024-09-05T19:52:09Z)
• A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds [49.34500499203579]
  We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics. We generate high-quality synthetic fMRI data based on user-supplied demographics.
  arXiv  Detail & Related papers  (2024-05-13T17:49:20Z)
• Predicting Parkinson's disease evolution using deep learning [1.4610685586329806]
  Parkinson's disease is a neurological condition that occurs in nearly 1% of the world's population. There is not a single blood test or biomarker available to diagnose Parkinson's disease. No AI tools have been designed to identify the stage of progression.
  arXiv  Detail & Related papers  (2023-12-28T10:30:54Z)
• Modeling T1 Resting-State MRI Variants Using Convolutional Neural Networks in Diagnosis of OCD [0.0]
  Obsessive-compulsive disorder (OCD) presents itself as a highly debilitating disorder. OCD has common associations with the prefrontal cortex and the glutamate receptor known as Metabotropic Glutamate Receptor 5 (mGluR5) Computational modeling methods were used as a means of validation for previous hypotheses involving mGluR5.
  arXiv  Detail & Related papers  (2023-06-15T12:02:57Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers [72.047680167969]
  This article aims to introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods. We will detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies.
  arXiv  Detail & Related papers  (2022-04-01T22:48:08Z)
• Early Disease Stage Characterization in Parkinson's Disease from Resting-state fMRI Data Using a Long Short-term Memory Network [6.487961959149217]
  Parkinson's disease (PD) is a common and complex neurodegenerative disorder with 5 stages in the Hoehn and Yahr scaling. It is challenging to classify early stages 1 and 2 and detect brain function alterations. We propose the use of a long short-term memory (LSTM) network to characterize the early stages of PD.
  arXiv  Detail & Related papers  (2022-02-11T18:34:11Z)
• Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging [77.32032399775152]
  Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability.
  arXiv  Detail & Related papers  (2022-01-27T16:37:19Z)
• Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques [111.165389441988]
  This work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI) Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.
  arXiv  Detail & Related papers  (2021-05-18T11:37:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.