Kinematic analysis of lumbar pedicle screw placement using an artificial intelligence framework
This proof-of-concept study presents a data processing pipeline capable of analyzing metrics to quantify surgical proficiency during spinal procedures. The methods described demonstrate the feasibility of using AI-driven video analysis to assess hand motion. It also highlights specific motion-based metrics that can distinguish between FH and RB techniques and correlate with surgical training level. These findings lay the groundwork for developing a standardized, objective framework for proficiency assessment in spine surgery.
Authors
- Stanley Hoang (ORCID: https://orcid.org/0000-0002-3096-5175)
- Christian Quinones (ORCID: https://orcid.org/0000-0002-4073-2092)
- Deepak Kumbhare (ORCID: https://orcid.org/0000-0003-2514-9576)
- Bharat Guthikonda (ORCID: https://orcid.org/0000-0001-6668-6656)
- Subhajit Chakrabarty (ORCID: https://orcid.org/0000-0003-0818-3190)
- Devesh Sarda (ORCID: https://orcid.org/0009-0008-3301-0674)
- Udaysinh Rathod (ORCID: https://orcid.org/0009-0002-5391-2436)
- Matthew Palfreeman
Institutions
- Louisiana State University in Shreveport (US)
- Louisiana State University Health Sciences Center Shreveport (US)
Publication Details
- Journal
- Neurosurgical FOCUS
- Published
- 2025-07-01
- DOI
- https://doi.org/10.3171/2025.4.focus25157
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00