Prediction of lower limb compensation for adult degenerative spine patients using spinopelvic parameters: a machine learning approach
Mohammad Daher, Zhi Wang, Victor Baisamy, Philippe Phan, Carlos Vazquez, Thierry Cresson, Fidaa Al Shakfa, Alan H Daniels, Amer Sebaaly · 2026
Read Prediction of lower limb compensation for adult degenerative spine patients using spinopelvic parameters: a machine learning approach by Mohammad Daher, Zhi Wang, Victor Baisamy, Philippe Phan, Carlos Vazquez, Thierry Cresson, Fidaa Al Shakfa, Alan H Daniels, Amer Sebaaly (2026) 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/PMC13536625.txt.
Description
Mohammad Daher and colleagues test in 2026 whether spine and pelvis measures alone can predict knee bending compensation in adults with degenerative spine disease. Bent knees are a common way patients offset a tilted trunk, and spotting them guides surgery planning. Records covered 201 surgical patients, with spine measures taken from standing X-ray images.
Knee measures were left out of the models on purpose to test true prediction. A support vector machine classified knee bending with fair accuracy and high sensitivity on test data. The authors say spine measures can flag compensation, though larger uniform groups should confirm the approach first.
Catalog record
| CREATOR | Mohammad Daher, Zhi Wang, Victor Baisamy, Philippe Phan, Carlos Vazquez, Thierry Cresson, Fidaa Al Shakfa, Alan H Daniels, Amer Sebaaly |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 27605 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |