Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images

Area of Science:

• Orthopedics
• Radiology
• Artificial Intelligence

Background:

• Knee Osteoarthritis (OA) is a prevalent musculoskeletal condition.
• Early-stage OA management includes lifestyle changes, while late-stage often requires total knee replacement (TKR).
• Current TKR outcomes are variable, with complications and dissatisfaction necessitating better risk prediction.

Purpose of the Study:

• To develop a deep learning pipeline for predicting TKR risk.
• To identify patients at higher risk for TKR, especially at earlier OA stages.
• To discover imaging biomarkers associated with OA progression and TKR.

Main Methods:

• A deep learning pipeline integrating MRI scans with clinical and demographic data.
• Model performance evaluated using Area Under the Curve (AUC) with statistical significance testing.
• Occlusion mapping used to identify key imaging regions predictive of TKR.

Main Results:

• The pipeline achieved an overall TKR prediction AUC of 0.834 ± 0.036.
• Notably, TKR was predicted with an AUC of 0.943 ± 0.057 in patients without diagnosed OA.
• Occlusion maps revealed distinct imaging biomarkers differentiating TKR risk.

Conclusions:

• The developed deep learning model shows significant potential for clinical utility in TKR risk prediction.
• Identified imaging biomarkers enhance understanding of OA progression towards TKR.
• This approach could facilitate timely interventions to slow OA progression and delay TKR.