Machine learning and deep neural network-based learning in osteoarthritis knee.

Area of Science:

• Orthopedics
• Biomedical Engineering
• Data Science

Background:

• Knee osteoarthritis is a leading cause of disability worldwide.
• Machine learning (ML) offers significant advancements in osteoarthritis research.
• ML aids in early diagnosis, phenotype discovery, and progression prediction.

Purpose of the Study:

• To review the applications of machine learning in knee osteoarthritis research.
• To highlight ML's role in early detection and disease management.
• To identify current limitations and future research directions for ML in OA.

Main Methods:

• Utilizing machine learning for analyzing morphological, molecular, electrical, and mechanical features.
• Applying ML with non-invasive imaging techniques like magnetic resonance imaging (MRI).
• Leveraging large cohort studies such as the OA Initiative and MOAST.

Main Results:

• ML enables early diagnosis of knee osteoarthritis.
• ML assists in identifying OA phenotypes and predicting disease progression.
• ML techniques are being applied to large observational studies for deeper insights.

Conclusions:

• Machine learning shows immense potential in transforming knee osteoarthritis research and clinical practice.
• Further studies are crucial to overcome limitations and optimize ML applications.
• Integrating ML can lead to improved clinical decision-making and timely interventions for knee OA.