A Specimen-Separated Machine Learning Benchmark Toward Real-Time Tissue-Type Identification in Guided Surgery Using Ex Vivo Bovine Laser-Induced Breakdown Spectroscopy.

René Fernando Sosa-Santos, José Luis Arce-Diego, Félix Fanjul-Vélez

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

René Fernando Sosa-SantosBiomedical Engineering Group, TEISA Department, Universidad de Cantabria, 39005 Santander, Spain.ORCID 0009-0004-0339-8402

José Luis Arce-DiegoBiomedical Engineering Group, TEISA Department, Universidad de Cantabria, 39005 Santander, Spain.ORCID 0000-0002-5403-4404

Félix Fanjul-VélezBiomedical Engineering Group, TEISA Department, Universidad de Cantabria, 39005 Santander, Spain.ORCID 0000-0003-0739-3946

Funding

Fundación CarolinaGovernment of CantabriaSpanish Ministry of Science and Innovation PID2021-127691OB-I00

6 · The paper itself

Abstract

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,

Indexed as

LasersMachine LearningSpectrum AnalysisSurgery, Computer-AssistedAnimalsCattleClassification AlgorithmsSupport Vector Machinedata leakageextra treesgroup-based cross-validationguided surgeryintraoperative tissue identificationlaser-induced breakdown spectroscopymachine learningrandom forestsurgical margin assessmenttissue classification

Identifiers

PMID42655330

DOI10.3390/s26165020

PMCPMC13517582

What OpenQuestion holds

Textmetadata

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.