Machine-Learning Augmented Optical Tissue Sensing for Intraoperative Guidance in Spine Surgery: A Systematic Review and Meta-Analysis

Background: Pedicle-screw malposition and canal breach can cause major neurologic and vascular complications. Fluoroscopy, navigation, and robotics guide trajectory, but they do not directly identify the tissue in front of the instrument. We evaluated machine learning (ML)-augmented optical sensing for tissue discrimination in spine-relevant settings. Methods: We conducted a PRISMA-DTA systematic review under a prospectively registered PROSPERO protocol and searched five databases. Classification accuracy was pooled with random-effects models and reported with 95% confidence intervals (CI) and prediction intervals (PI). Study quality was assessed with QUADAS-2 and separate ML-specific signaling questions. Results: Eight studies met the inclusion criteria. Five single-group ex vivo or cadaveric classification studies entered the meta-analysis, four using porcine tissue. Across individual spectra, image patches, or images, pooled classification accuracy was 95.9% (95% CI, 93.5–97.7%), with substantial between-study variation (I2 = 99.4%; 95% PI, 89.8–99.4%). A sensitivity analysis using subject counts as the denominator yielded 97.36% (95% CI, 87.46–100.00%). Because outcomes were not reported at the subject-level, this estimate should not be read as patient-level accuracy. No study tested intraoperative tissue classification in living humans or performed external validation, and one had a high risk of data leakage. Two non-ML perfusion studies were retained only to describe a prespecified evidence gap. Conclusions: ML-augmented optical sensing can distinguish spine-relevant tissues in controlled preclinical settings. Current evidence does not establish intraoperative or patient-level performance. The literature remains small, heterogeneous, predominantly preclinical, and without external validation. Prospective in vivo human studies with independent validation are needed before these systems can support intraoperative decision-making.