A Specimen-Separated Machine Learning Benchmark Toward Real-Time Tissue-Type Identification in Guided Surgery Using...

Real-time tissue identification during laser-guided surgery is a critical unmet need for collateral damage avoidance and margin delineation. Laser-Induced Breakdown Spectroscopy (LIBS) is compatible with pulsed laser surgical systems and offers rapid, label-free elemental analysis. This study presents a machine learning pipeline classifying five ex vivo bovine tissue classes, plus one synthetic null-signal control class, from LIBS spectra, designed to control specimen-level data leakage and class imbalance bias. Key contributions are (i) a 'peak max over baseline' aggregation strategy suppressing shot noise while preserving emission peaks; (ii) a repeated, group-based cross-validation protocol (GroupShuffleSplit, N = 10) enforcing specimen-level separation; and (iii) a comparison of 30 configurations (10 classifiers × 3 pipelines). Extra Trees with normalization reached the highest weighted F1-score (0.934 ± 0.118); excluding the synthetic control, five-class scores fall to 0.875-0.915 and the ranking changes, so these are the reference figures for biological tissue discrimination. Support Vector Machines were less accurate but more consistent (0.917 ± 0.069). Acquisition takes approximately 3 s per point; inference is sub-millisecond. With five source animals, the best configuration chosen on the same outer splits, and inner tuning that was not group-aware, these estimates are an exploratory step toward real-time guided surgery.