Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning

• URL: http://arxiv.org/abs/2410.21512v1
• Date: Mon, 28 Oct 2024 20:31:27 GMT
• Title: Diagnosis of Knee Osteoarthritis Using Bioimpedance and Deep Learning
• Authors: Jamal Al-Nabulsi, Mohammad Al-Sayed Ahmad, Baraa Hasaneiah, Fayhaa AlZoubi,
• Abstract summary: Diagnosing knee osteoarthritis (OA) early is crucial for managing symptoms and preventing further joint damage. In this paper, a bioimpedance-based diagnostic tool that combines precise hardware and deep learning is proposed.
• Score: 0.0
• License:
• Abstract: Diagnosing knee osteoarthritis (OA) early is crucial for managing symptoms and preventing further joint damage, ultimately improving patient outcomes and quality of life. In this paper, a bioimpedance-based diagnostic tool that combines precise hardware and deep learning for effective non-invasive diagnosis is proposed. system features a relay-based circuit and strategically placed electrodes to capture comprehensive bioimpedance data. The data is processed by a neural network model, which has been optimized using convolutional layers, dropout regularization, and the Adam optimizer. This approach achieves a 98% test accuracy, making it a promising tool for detecting knee osteoarthritis musculoskeletal disorders.

Related papers

• MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study [0.7751705157998379]
  Alzheimer's disease (AD) and vascular dementia (VaD) are the two most prevalent dementia types. This paper presents an innovative multi-omics approach to accurately differentiate AD from VaD, achieving a diagnostic accuracy of 89.25%.
  arXiv  Detail & Related papers  (2024-11-06T10:13:28Z)
• 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)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• A multimodal method based on cross-attention and convolution for postoperative infection diagnosis [0.0]
  Postoperative infection diagnosis is a common and serious complication that poses a high diagnostic challenge. X-ray examination is an imaging examination for suspected PJI patients. In this study, we proposed a self-supervised masked autoencoder pre-training strategy and a multimodal fusion diagnostic network MED-NVC.
  arXiv  Detail & Related papers  (2023-05-23T15:08:56Z)
• A marker-less human motion analysis system for motion-based biomarker discovery in knee disorders [60.99112047564336]
  The NHS has been having increased difficulty seeing all low-risk patients, this includes but not limited to suspected osteoarthritis (OA) patients. We propose a novel method of automated biomarker identification for diagnosis of knee disorders and the monitoring of treatment progression.
  arXiv  Detail & Related papers  (2023-04-26T16:47:42Z)
• A Confident Labelling Strategy Based on Deep Learning for Improving Early Detection of Knee OsteoArthritis [9.400820679110147]
  Knee OsteoArthritis (KOA) is a prevalent musculoskeletal disorder that causes decreased mobility in seniors. In this paper, we propose a novel Siamese-based network, and we introduce a new hybrid loss strategy for the early detection of KOA.
  arXiv  Detail & Related papers  (2023-03-23T11:57:50Z)
• Exploring linguistic feature and model combination for speech recognition based automatic AD detection [61.91708957996086]
  Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Scarcity of specialist data leads to uncertainty in both model selection and feature learning when developing such systems. This paper investigates the use of feature and model combination approaches to improve the robustness of domain fine-tuning of BERT and Roberta pre-trained text encoders.
  arXiv  Detail & Related papers  (2022-06-28T05:09:01Z)
• NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization [59.15047491202254]
  symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition. We propose a new approach based on the supervised learning of neural models with logic regularization. Our experiments show that the proposed approach outperforms the best existing methods in the accuracy of diagnosis when the number of diagnoses and symptoms is large.
  arXiv  Detail & Related papers  (2022-06-02T07:57:17Z)
• Review of Machine Learning Algorithms for Brain Stroke Diagnosis and Prognosis by EEG Analysis [50.591267188664666]
  Strokes are the leading cause of adult disability in the United States. Brain-Computer Interfaces (BCIs) help the patient either restore neurologic pathways or effectively communicate with an electronic prosthetic. The various machine learning techniques and algorithms that are addressed and combined with BCIs technology show that the use of BCIs for stroke treatment is a promising and rapidly expanding field.
  arXiv  Detail & Related papers  (2020-08-06T19:50:29Z)

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.