Defining surgical strokes in lumbar laminectomy: toward objective skill assessment and robotic integration.

PURPOSE: To characterize discrete drilling stroke phenotypes during lumbar laminectomy using quantitative motion features, and to assess how phenotype usage varies by procedural stage, training level, and navigation assistance.

METHODS

Eleven orthopedic trainees each performed 10 lumbar laminectomies (110 total) on a CT-based VR simulator (CAPTAiN), split between CAPTAiN-assisted and non-navigated conditions. Burr strokes were quantified by length, velocity, acceleration, jerk, bone removed, and scaled proximity to the dura. Stroke feature vectors were embedded using UMAP and clustered via k-means (k = 3) into phenotypes. Phenotype distributions were compared across PGY level, procedural time quartiles (Q1-Q4), and navigation status using Bonferroni-corrected chi-squared tests (p < 0.05).

RESULTS

Clustering identified three stroke phenotypes: Exploratory (long, fast, high jerk, near dura), Debulking (moderate length/speed, greatest bone removal, farthest from dura), and Refinement (short, controlled, minimal bone removal). Analysis of procedural time quartiles showed a shift from Exploratory strokes in Q1 to Debulking dominance in Q2-Q4, with Refinement stable across time. Exploratory strokes decreased from 53.1% at 1 year to 19.3% at 4 years (p < 1.17 × 10- 8), while Debulking rose from 27.0% to > 51% (p < 2.74 × 10- 5), and Refinement increased from 19.7% to 37.3% (p = 0.0052). Navigation reduced Exploratory strokes (28.9% vs. 39.2%, p = 0.0046) and increased Debulking (51.6% vs. 45.3%) and Refinement (20.1% vs. 16.4%, p = 0.032).

CONCLUSIONS

VR-based motion analysis identifies three drilling stroke phenotypes for lumbar laminectomy that can guide surgical skill assessment, trainee feedback, and future applications in automated robotic surgery and simulation-based training.