Intelligent Scoliosis Screening and Diagnosis: A Survey

• URL: http://arxiv.org/abs/2310.08756v2
• Date: Sun, 22 Sep 2024 19:57:08 GMT
• Title: Intelligent Scoliosis Screening and Diagnosis: A Survey
• Authors: Zhenlin Zhang, Lixin Pu, Ang Li, Jun Zhang, Xianjie Li, Jipeng Fan,
• Abstract summary: The number of scoliosis patients in primary and secondary schools has exceeded five million in China. The incidence rate is about 3% to 5% which is growing every year. This paper systematically introduces computer-assisted scoliosis screening and diagnosis.
• Score: 9.01107988952023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scoliosis is a three-dimensional spinal deformity, which may lead to abnormal morphologies, such as thoracic deformity, and pelvic tilt. Severe patients may suffer from nerve damage and urinary abnormalities. At present, the number of scoliosis patients in primary and secondary schools has exceeded five million in China, the incidence rate is about 3% to 5% which is growing every year. The research on scoliosis, therefore, has important clinical value. This paper systematically introduces computer-assisted scoliosis screening and diagnosis as well as analyzes the advantages and limitations of different algorithm models in the current issue field. Moreover, the paper also discusses the current development bottlenecks in this field and looks forward to future development trends.

Related papers

• Detection of adrenal anomalous findings in spinal CT images using multi model graph aggregation [0.0]
  Low back pain is the second most frequently reported to primary care physicians. Low back pain affects 50 to 80 percent of the population in a lifetime.
  arXiv  Detail & Related papers  (2024-10-27T19:45:15Z)
• Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports [51.45762396192655]
  Multimodal large language models (MLLMs) have recently transformed many domains, significantly affecting the medical field. Notably, Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models have epitomized a paradigm shift in Artificial General Intelligence for computer vision. This study evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets.
  arXiv  Detail & Related papers  (2024-07-08T09:08:42Z)
• Gait Patterns as Biomarkers: A Video-Based Approach for Classifying Scoliosis [10.335383345968966]
  Scoliosis presents significant diagnostic challenges, particularly in adolescents. Traditional diagnostic and follow-up methods face limitations due to the need for clinical expertise and the risk of radiation exposure. We introduce a novel video-based, non-invasive method for scoliosis classification using gait analysis.
  arXiv  Detail & Related papers  (2024-07-08T08:29:02Z)
• Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding [51.38395069380457]
  Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities facilitates proactive measures to mitigate the risk of severe back pain and disability. In this study, we specifically explore the use of shape auto-encoders for vertebrae.
  arXiv  Detail & Related papers  (2023-12-08T18:11:22Z)
• Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning [48.29204631769816]
  We re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification. Our experimental results show that models considering disease severity and uncertainty outperform previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-06T19:19:41Z)
• 3D Patient-specific Modelling and Characterisation of Muscle-Skeletal Districts [0.0]
  We propose different methods for the integration of morphological information, retrieved from the geometrical analysis of 3D surface models. For the qualitative and quantitative validation, we will discuss the localisation of bone erosion sites on the wrists to monitor rheumatic diseases. The proposed approach supports the quantitative and visual evaluation of possible damages, surgery planning, and early diagnosis or follow-up studies.
  arXiv  Detail & Related papers  (2023-04-18T21:46:42Z)
• Scoliosis Detection using Deep Neural Network [0.0]
  Scoliosis is a sideways curvature of the spine that most often is diagnosed among young teenagers. Current gold standard to detect and estimate scoliosis is to manually examine the spinal anterior-posterior X-ray images. Deep learning has shown amazing achievements in automatic spinal curvature estimation.
  arXiv  Detail & Related papers  (2022-10-31T12:52:04Z)
• 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)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• 3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT [1.7372615815088566]
  An osteoporosis-related fracture occurs every three seconds worldwide, affecting one in three women and one in five men aged over 50. In this study, we present an automatic system for identification of vertebral compression fractures on Computed Tomography images. The system integrates a compact 3D representation of the spine, utilizing a Convolutional Neural Network (CNN) for spinal cord detection and a novel end-to-end sequence to sequence 3D architecture.
  arXiv  Detail & Related papers  (2020-10-08T02:39:40Z)
• Patch-based Brain Age Estimation from MR Images [64.66978138243083]
  Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. We develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator.
  arXiv  Detail & Related papers  (2020-08-29T11:50:37Z)

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.