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

2025-08-13

AI Summary

This study investigates the association between radiographic alignment parameters—such as the hip-knee-ankle (HKA) angle and tibial slope—and functional outcomes and pain following total knee replacement (TKR). It systematically identifies and validates clinically predictive alignment biomarkers. We propose the first AI-driven automated alignment assessment framework, integrating deep learning with medical image analysis to enable high-accuracy, high-reproducibility measurement of key alignment parameters from X-ray images, and subsequently link these measurements to clinical outcome scores (e.g., Oxford Knee Score, WOMAC) via prognostic modeling. Unlike prior reviews, this work uniquely bridges alignment pathophysiology, clinical prognostic evidence, and advances in AI methodology—addressing a critical gap at the intersection of alignment, prognosis, and artificial intelligence. Results demonstrate that AI-assisted alignment quantification significantly improves both assessment efficiency and prognostic accuracy, establishing a novel paradigm for intelligent orthopedic diagnosis and treatment.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Investigates knee alignment's impact on replacement outcomes
• Explores AI-driven radiographic knee alignment assessment
• Reviews biomarkers for predicting knee replacement success

Innovation

• AI automates knee alignment measurements
• AI analyzes radiographs for TKR biomarkers
• Review explores AI-driven TKR outcome prediction