Assessing the Utility and Challenges of Machine Learning in Spinal Deformity Management
The current state of machine learning in scoliosis management is promising, showing potential for improving diagnosis and optimizing treatments. However, significant challenges remain. To fully understand the benefits of ML, larger, multi-center studies with more validation and effective implementation strategies are needed.
Authors
- Amro Hajja
- Joseph E. Nassar (ORCID: https://orcid.org/0000-0002-2475-4996)
- Daniel Alsoof (ORCID: https://orcid.org/0000-0002-8754-7070)
- Faisal Konbaz (ORCID: https://orcid.org/0000-0001-7274-8112)
- Muhammad M. Abd‐El‐Barr (ORCID: https://orcid.org/0000-0001-7151-2861)
- Jibran Khan (ORCID: https://orcid.org/0000-0002-6109-7470)
- Anouar Bourghli (ORCID: https://orcid.org/0000-0002-2852-0230)
- Zeeshan M. Sardar (ORCID: https://orcid.org/0000-0002-8417-3798)
- Bassel G. Diebo
- Abdelrafour Houdane
Institutions
- Alfaisal University (SA)
- Brown University (US)
- Columbia University Irving Medical Center (US)
- Texas Health Dallas (US)
- King Faisal Specialist Hospital & Research Centre (SA)
- Duke Medical Center (US)
- Duke University Hospital (US)
Publication Details
- Journal
- Spine
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1097/brs.0000000000005236
- Citations
- 4
- Primary Topic
- Scoliosis diagnosis and treatment
- Type
- article
- Field-Weighted Citation Impact
- 8.03