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Related Experiment Video

Updated: Jul 15, 2026

The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals
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Artificial Intelligence Applications in Degenerative Musculoskeletal Diseases.

Laura E Garton1, Scott J Billings1, Ryan W Schwertner1

  • 1Department of Radiology, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, FL 32224, USA.

Magnetic Resonance Imaging Clinics of North America
|July 13, 2026
PubMed
Summary

Artificial intelligence (AI) shows promise in assessing degenerative musculoskeletal disorders like knee osteoarthritis and lumbar spine degeneration. While AI matches expert performance in some areas, challenges remain in early disease detection and standardized methods for spine imaging.

Keywords:
Artificial intelligence (AI)Deep learning (DL)Disease gradingKnee osteoarthritis (OA)Lumbar spine degenerationMagnetic resonance imagingRadiography

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A Novel Application of Musculoskeletal Ultrasound Imaging
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Published on: September 17, 2013

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A Novel Application of Musculoskeletal Ultrasound Imaging
10:53

A Novel Application of Musculoskeletal Ultrasound Imaging

Published on: September 17, 2013

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Musculoskeletal Disorders

Background:

  • Degenerative musculoskeletal disorders, including knee osteoarthritis and lumbar spine degeneration, are major causes of disability and healthcare expenditure.
  • Increasing imaging volumes necessitate efficient and consistent assessment methods.
  • Artificial intelligence (AI) is being explored to enhance medical image analysis.

Purpose of the Study:

  • To evaluate the performance of AI models in assessing knee osteoarthritis and lumbar spine degeneration using imaging data.
  • To identify current limitations and future directions for AI in musculoskeletal imaging.

Main Methods:

  • Review of AI model performance in knee osteoarthritis assessment using radiographs and MR imaging.
  • Analysis of AI applications in lumbar spine MR imaging for grading and segmentation.
  • Consideration of factors influencing AI progress in this field.

Main Results:

  • AI models demonstrate radiologist-level performance for knee osteoarthritis, but early classification remains difficult.
  • AI shows potential for automating lumbar spine imaging tasks, though methods are inconsistent.
  • Heterogeneity in AI methods currently limits widespread application in spine imaging.

Conclusions:

  • AI holds significant potential for improving the efficiency and consistency of musculoskeletal imaging assessment.
  • Standardized frameworks, diverse datasets, external validation, and outcome integration are crucial for advancing AI in this domain.
  • Further research is needed to overcome current challenges in early disease detection and methodological standardization.