Explainable machine learning for orthopedic decision-making: predicting functional outcomes of total hip replacement from gait biomechanics
Bernd J. Stetter, Jonas Dully, Felix Stief, Jana Holder, Hannah Steingrebe, Frank Zaucke, Stefan Sell, Stefan van Drongelen, Thorsten Stein · 2025
Read Explainable machine learning for orthopedic decision-making: predicting functional outcomes of total hip replacement from gait biomechanics by Bernd J. Stetter, Jonas Dully, Felix Stief, Jana Holder, Hannah Steingrebe, Frank Zaucke, Stefan Sell, Stefan van Drongelen, Thorsten Stein (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12743397.txt.
Description
Bernd Stetter and co-authors published this study in 2025. It uses explainable machine learning to sort hip patients by how they walk. The team recorded 3D gait patterns from 109 people with hip osteoarthritis before joint replacement. Sixty-three were measured again after surgery, and fifty-six healthy people served as controls. Clustering split the patients into three groups with distinct walking styles. The groups also differed in age, joint damage scores, and walking speed.
Before surgery, half to most patients in each group walked in a flagged abnormal pattern. After surgery, that share fell by roughly half. Hip bending and rotation measures drove the sorting most. Gains after surgery varied by group. The authors suggest sorting patients before surgery could forecast recovery and tailor rehab to each walking style.
Catalog record
| CREATOR | Bernd J. Stetter, Jonas Dully, Felix Stief, Jana Holder, Hannah Steingrebe, Frank Zaucke, Stefan Sell, Stefan van Drongelen, Thorsten Stein |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 68 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |