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Related Concept Videos

Knee Joint01:23

Knee Joint

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The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...
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Author Spotlight: Fu's Subcutaneous Needling for Knee Osteoarthritis Pain
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Machine learning in knee osteoarthritis: A review.

C Kokkotis1,2, S Moustakidis3,4, E Papageorgiou1,3

  • 1Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, Volos, Greece.

Osteoarthritis and Cartilage Open
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Summary

Machine Learning (ML) techniques are crucial for diagnosing and predicting knee osteoarthritis. This review explores ML applications in knee osteoarthritis, aiding in better treatment strategies.

Keywords:
ClassificationFeature engineeringKnee osteoarthritisMachine learningPredictionSegmentation

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Orthopedics

Background:

  • Knee osteoarthritis (OA) presents significant challenges due to complex, heterogeneous, and large datasets.
  • Machine Learning (ML) has emerged as a vital tool for addressing these challenges in OA research.

Purpose of the Study:

  • To provide a comprehensive overview of Machine Learning techniques applied to knee osteoarthritis diagnosis and prediction.
  • To categorize ML applications in knee OA based on their domain.

Main Methods:

  • A systematic review of research articles published between 2006 and 2019.
  • Categorization of studies into four domains: predictions/regression, classification, optimum post-treatment planning, and segmentation.

Main Results:

  • The review outlines the characteristics of ML algorithms used, their application domains, data sources, and performance.
  • Identified trends and key directions in ML for knee OA research.

Conclusions:

  • Machine Learning offers powerful solutions for managing knee osteoarthritis as a big data problem.
  • ML facilitates the development of automated pre- and post-treatment strategies using diverse data sources.