Accurate early detection of Parkinson's disease from SPECT imaging through Convolutional Neural Networks

• URL: http://arxiv.org/abs/2412.05348v1
• Date: Fri, 06 Dec 2024 16:14:02 GMT
• Title: Accurate early detection of Parkinson's disease from SPECT imaging through Convolutional Neural Networks
• Authors: R. Prashanth,
• Abstract summary: Early and accurate detection of Parkinson's disease (PD) is a crucial diagnostic challenge carrying immense clinical significance. In this work, machine learning models are developed using features from SPECT images to detect early PD and SWEDD subjects from normal.
• Score: 0.0
• License:
• Abstract: Early and accurate detection of Parkinson's disease (PD) is a crucial diagnostic challenge carrying immense clinical significance, for effective treatment regimens and patient management. For instance, a group of subjects termed SWEDD who are clinically diagnosed as PD, but show normal Single Photon Emission Computed Tomography (SPECT) scans, change their diagnosis as non-PD after few years of follow up, and in the meantime, they are treated with PD medications which do more harm than good. In this work, machine learning models are developed using features from SPECT images to detect early PD and SWEDD subjects from normal. These models were observed to perform with high accuracy. It is inferred from the study that these diagnostic models carry potential to help PD clinicians in the diagnostic process

Related papers

• Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling [49.52787013516891]
  Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.
  arXiv  Detail & Related papers  (2024-05-14T17:15:28Z)
• Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis [59.35504779947686]
  GPT-4V is OpenAI's newest model for multimodal medical diagnosis. Our evaluation encompasses 17 human body systems. GPT-4V demonstrates proficiency in distinguishing between medical image modalities and anatomy. It faces significant challenges in disease diagnosis and generating comprehensive reports.
  arXiv  Detail & Related papers  (2023-10-15T18:32:27Z)
• A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals [8.526741765074677]
  This study presents a deep learning-based model for the diagnosis of Parkinson's disease (PD) using resting state electroencephalogram (EEG) signal. The model is designed using a hybrid model, consists of convolutional neural network (CNN), bidirectional gated recurrent unit (Bi-GRU) and attention mechanism. The results show that the proposed model can accurately diagnose PD with high performance on both the training and hold-out datasets.
  arXiv  Detail & Related papers  (2023-08-14T20:06:19Z)
• Parkinsons Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques [0.0]
  Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor deficits due to advancing degeneration of dopaminergic neurons. EEG indicates abnormalities in PD patients. One major challenge is the lack of a consistent, accurate, and systemic biomarker for PD in order to closely monitor the disease with therapeutic treatments and medication.
  arXiv  Detail & Related papers  (2023-03-29T06:03:05Z)
• Intelligent Sight and Sound: A Chronic Cancer Pain Dataset [74.77784420691937]
  This paper introduces the first chronic cancer pain dataset, collected as part of the Intelligent Sight and Sound (ISS) clinical trial. The data collected to date consists of 29 patients, 509 smartphone videos, 189,999 frames, and self-reported affective and activity pain scores. Using static images and multi-modal data to predict self-reported pain levels, early models show significant gaps between current methods available to predict pain.
  arXiv  Detail & Related papers  (2022-04-07T22:14:37Z)
• 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)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• Parkinson's Disease Diagnosis Using Deep Learning [0.0]
  Parkinson's Disease (PD) is a chronic, degenerative disorder which leads to a range of motor and cognitive symptoms. This project aims to automate the PD diagnosis process using deep learning, Recursive Neural Networks (RNN) and Convolutional Neural Networks (CNN)
  arXiv  Detail & Related papers  (2021-01-03T18:39:25Z)
• Convolutional neural networks for automatic detection of Focal Cortical Dysplasia [59.034649152318224]
  Focal cortical dysplasia (FCD) is one of the most common epileptogenic lesions associated with cortical development malformations. Recent methods of Deep Learning-based FCD detection apply it for a dataset of 15 labeled FCD patients. The model results in the successful detection of FCD on 11 out of 15 subjects.
  arXiv  Detail & Related papers  (2020-10-20T15:30:37Z)
• Machine learning for the diagnosis of Parkinson's disease: A systematic review [15.463800489731373]
  We conducted a systematic literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this systematic review. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making.
  arXiv  Detail & Related papers  (2020-10-13T01:14:04Z)
• An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery [0.0]
  Parkinson's disease (PD) is a degenerative and progressive neurological condition. Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTscan. In this study, we propose a machine learning model that accurately classifies any given DaTscan as having Parkinson's disease or not.
  arXiv  Detail & Related papers  (2020-08-01T10:44:03Z)

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.