Evidence map›Paper›PMID 37836924›Full record

SynthesisSensors (Basel, Switzerland)2023

State-of-the-Art of Non-Radiative, Non-Visual Spine Sensing with a Focus on Sensing Forces, Vibrations and Bioelectrical Properties: A Systematic Review.

Maikel Timmermans, Aidana Massalimova, Ruixuan Li, Ayoob Davoodi, Quentin Goossens, Kenan Niu, Emmanuel Vander Poorten, Philipp Fürnstahl, Kathleen Denis

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025
    Review
  3. Article
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

9 authors at 2 institutions in 2 countries.

Maikel TimmermansKU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation, 3000 Leuven, Belgium.
Aidana MassalimovaResearch in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, 8008 Zurich, Switzerland.ORCID 0000-0002-6203-1270
Ruixuan LiKU Leuven, Department of Mechanical Engineering, Robot-Assisted Surgery Group (RAS), 3000 Leuven, Belgium.ORCID 0000-0003-2882-8398
Ayoob DavoodiKU Leuven, Department of Mechanical Engineering, Robot-Assisted Surgery Group (RAS), 3000 Leuven, Belgium.
Quentin GoossensKU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation, 3000 Leuven, Belgium.
Kenan NiuKU Leuven, Department of Mechanical Engineering, Robot-Assisted Surgery Group (RAS), 3000 Leuven, Belgium.ORCID 0000-0002-5498-4311
Emmanuel Vander PoortenKU Leuven, Department of Mechanical Engineering, Robot-Assisted Surgery Group (RAS), 3000 Leuven, Belgium.
Philipp FürnstahlResearch in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, 8008 Zurich, Switzerland.
Kathleen DenisKU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation, 3000 Leuven, Belgium.ORCID 0000-0002-6492-9607
KU Leuven · BEUniversity of Zurich · CH

Funding

European Union's Horizon 2020 101016985FWO Vlaanderen 1S35422N
6 · The paper itself

Abstract

In the research field of robotic spine surgery, there is a big upcoming momentum for surgeon-like autonomous behaviour and surgical accuracy in robotics which goes beyond the standard engineering notions such as geometric precision. The objective of this review is to present an overview of the state of the art in non-visual, non-radiative spine sensing for the enhancement of surgical techniques in robotic automation. It provides a vantage point that facilitates experimentation and guides new research projects to what has not been investigated or integrated in surgical robotics. Studies were identified, selected and processed according to the PRISMA guidelines. Relevant study characteristics that were searched for include the sensor type and measured feature, the surgical action, the tested sample, the method for data analysis and the system's accuracy of state identification. The 6DOF f/t sensor, the microphone and the electromyography probe were the most commonly used sensors in each category, respectively. The performance of the electromyography probe is unsatisfactory in terms of preventing nerve damage as it can only signal after the nerve is disturbed. Feature thresholding and artificial neural networks were the most common decision algorithms for state identification. The fusion of different sensor data in the decision algorithm improved the accuracy of state identification.

Indexed as

RoboticsRobotic Surgical ProceduresRotationSpineVibrationbioelectricalforceroboticsensorspine surgerysystematic reviewvibration

Identifiers

PMID37836924
PMCPMC10574884
OpenAlexW4387101506

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.