AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions
Issued Date
2026-12-01
Resource Type
ISSN
03445607
eISSN
14372320
Scopus ID
2-s2.0-105033478701
Journal Title
Neurosurgical Review
Volume
49
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Neurosurgical Review Vol.49 No.1 (2026)
Suggested Citation
Thintharua P., Prabrai R., khamsiriwatchara A., Sethi R., Chumnanvej S. AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions. Neurosurgical Review Vol.49 No.1 (2026). doi:10.1007/s10143-026-04192-2 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115908
Title
AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions
Corresponding Author(s)
Other Contributor(s)
Abstract
Artificial intelligence (AI) is a general term that refers to the use of a computer to simulate intelligent behavior with minimal human intervention. Currently, AI can be applied to various spine surgery approaches. This review aims to provide a clearer picture of AI’s applicability for the perioperative period and enhance outcomes for pedicle screw fixation (PS). The PRISMA guideline was applied, which identified 14 studies regarding AI applications in PS. We categorized the AI application to PS into segmentation, object detection, image registration, and other categories, such as improved quality and converted images. Then, an analysis and discussion of the current trends and applications of various AI models in PS methods was performed. The effects of AI performance included a reduction in the time required for operations and planning, automatic identification of screws and anatomical landmarks, reduced image errors, and reduced radiation exposure. However, the lack of training data and less data diversity remain the limitations of model development, as both factors impact model generalization and robustness. This data extraction might reveal research gaps, providing researchers with ideas for future studies regarding AI and PS integration for better medical care outcomes.