Artificial Intelligence Applications in Degenerative Musculoskeletal Diseases

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.