The role of radiographic knee alignment in total knee replacement outcomes and opportunities for artificial intelligence-driven assessment
- 1Department of Informatics, Imaging and Data Sciences, The University of Manchester, UK
- 2Department of Trauma and Orthopaedics, Stockport NHS Foundation Trust, Stepping Hill Hospital, UK
- 3Weill Cornell Medicine, Cornell University, USA
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, and numerous factors may affect them. Radiographic knee alignment is a key factor affecting TKR success, affecting outcomes such as postoperative pain or function. Recently, artificial intelligence (AI) has been introduced to automatically analyse knee radiographs, for example, to assess knee alignment. These tools reduce measurement variability, support high-throughput analysis of large imaging datasets, and help identify alignment features associated with postoperative outcomes. A focused review is therefore needed to clarify how knee alignment biomarkers are measured, how they relate to TKR outcomes, and how AI-based approaches may support their assessment and clinical application in surgical planning and patient risk stratification. 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 AI-driven knee alignment assessment and TKR outcome prediction. Knee alignment biomarkers represent an important but underexplored domain for improving prediction of TKR outcomes. AI-driven radiographic assessment offers a promising means to generate consistent, scalable, and clinically actionable measures of alignment, though challenges remain in accuracy, validation, multi-view integration, and clinical deployment. Improved models, rigorous external validation, and systems that link alignment measurements to outcome prediction are needed to enhance reliability and clinical applicability.
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BibTeX
@article{hu2026role,
title = {{The role of radiographic knee alignment in total knee replacement outcomes and opportunities for artificial intelligence-driven assessment}},
author = {Zhisen Hu and Dominic Cullen and David S. Johnson and Aleksei Tiulpin and Timothy F. Cootes and Claudia Lindner},
journal = {Osteoarthritis and cartilage open},
year = {2026},
month = oct,
doi = {10.1016/j.ocarto.2026.100889},
url = {https://europepmc.org/article/PMC/PMC13717176},
note = {CC BY},
}
RIS
TY - JOUR
TI - The role of radiographic knee alignment in total knee replacement outcomes and opportunities for artificial intelligence-driven assessment
AU - Zhisen Hu
AU - Dominic Cullen
AU - David S. Johnson
AU - Aleksei Tiulpin
AU - Timothy F. Cootes
AU - Claudia Lindner
PY - 2026
DA - 2026/10/16
JO - Osteoarthritis and cartilage open
DO - 10.1016/j.ocarto.2026.100889
UR - https://europepmc.org/article/PMC/PMC13717176
AB - 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, and numerous factors may affect them. Radiographic knee alignment is a key factor affecting TKR success, affecting outcomes such as postoperative pain or function. Recently, artificial intelligence (AI) has been introduced to automatically analyse knee radiographs, for example, to assess knee alignment. These tools reduce measurement variability, support high-throughput analysis of large imaging datasets, and help identify alignment features associated with postoperative outcomes. A focused review is therefore needed to clarify how knee alignment biomarkers are measured, how they relate to TKR outcomes, and how AI-based approaches may support their assessment and clinical application in surgical planning and patient risk stratification. 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 AI-driven knee alignment assessment and TKR outcome prediction. Knee alignment biomarkers represent an important but underexplored domain for improving prediction of TKR outcomes. AI-driven radiographic assessment offers a promising means to generate consistent, scalable, and clinically actionable measures of alignment, though challenges remain in accuracy, validation, multi-view integration, and clinical deployment. Improved models, rigorous external validation, and systems that link alignment measurements to outcome prediction are needed to enhance reliability and clinical applicability.
SN - 2665-9131
ER -