The Role of Radiographic Knee Alignment in Knee Replacement Outcomes and Opportunities for Artificial Intelligence-Driven Assessment

• URL: http://arxiv.org/abs/2508.10941v1
• Date: Wed, 13 Aug 2025 12:09:20 GMT
• Title: The Role of Radiographic Knee Alignment in Knee Replacement Outcomes and Opportunities for Artificial Intelligence-Driven Assessment
• Authors: Zhisen Hu, David S. Johnson, Aleksei Tiulpin, Timothy F. Cootes, Claudia Lindner,
• Abstract summary: Total knee replacement (TKR) is the ultimate treatment for prevalent knee osteoarthritis (OA)<n>Radiographic knee alignment is one of the key factors that impacts TKR outcomes.
• Score: 0.8039067099377079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prevalent knee osteoarthritis (OA) imposes substantial burden on health systems with no cure available. Its ultimate treatment is total knee replacement (TKR). Complications from surgery and recovery are difficult to predict in advance, and numerous factors may affect them. Radiographic knee alignment is one of the key factors that impacts TKR outcomes, affecting outcomes such as postoperative pain or function. Recently, artificial intelligence (AI) has been introduced to the automatic analysis of knee radiographs, for example, to automate knee alignment measurements. Existing review articles tend to focus on knee OA diagnosis and segmentation of bones or cartilages in MRI rather than exploring knee alignment biomarkers for TKR outcomes and their assessment. In this review, we first examine the current scoring protocols for evaluating TKR outcomes and potential knee alignment biomarkers associated with these outcomes. We then discuss existing AI-based approaches for generating knee alignment biomarkers from knee radiographs, and explore future directions for knee alignment assessment and TKR outcome prediction.

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