Knee Osteoarthritis Diagnosis: Future and Perspectives

Area of Science:

• Orthopedics
• Medical Diagnostics
• Biomedical Engineering

Background:

• Symptomatic knee osteoarthritis (KOA) affects a significant portion of the population, with lifetime risks around 40-47%.
• Current diagnostic methods for KOA, including arthroscopy, MRI, and X-ray, often show discrepancies with patient symptoms.
• There is a critical need for efficient diagnostic tools to monitor, evaluate, and predict KOA progression.

Purpose of the Study:

• To review current and emerging diagnostic methods for knee osteoarthritis.
• To focus on non- or minimally invasive techniques for KOA assessment.
• To highlight the potential of novel technologies and Artificial Intelligence (AI) in KOA management.

Main Methods:

• Review of standard diagnostic tools: arthroscopy, MRI, X-ray radiography.
• Exploration of alternative strategies: biochemical markers, acoustic emission recordings.
• Investigation of promising non-invasive methods: electrical bioimpedance, near-infrared spectrometry.
• Assessment of Artificial Intelligence (AI) for predictive modeling in KOA.

Main Results:

• Standard imaging methods lack direct correlation with clinical symptoms.
• Non- or minimally invasive methods offer tissue condition data but have limitations like interference or lack of direct visualization.
• Electrical bioimpedance and near-infrared spectrometry show potential for cost-effective, portable, non-invasive joint tissue health monitoring.
• AI integration requires large, robust databases for effective predictive model development.

Conclusions:

• Novel diagnostic approaches are crucial for improving KOA monitoring and patient care.
• Non-invasive techniques and AI hold significant promise for personalized medicine in KOA.
• Further validation and implementation are needed for emerging technologies to revolutionize KOA diagnosis and treatment strategies.