Evidence map›Paper›PMID 30790048›Full record

ArticleSurgical endoscopy2019

Sensor-based machine learning for workflow detection and as key to detect expert level in laparoscopic suturing and knot-tying.

Karl-Friedrich Kowalewski, Carly R Garrow, Mona W Schmidt, Laura Benner, Beat P Müller-Stich, Felix Nickel

Abstract read
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Article in Surgical endoscopy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
6.0field-weighted citation impact, top 3% 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

34 citing papers in PubMed, 67 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Karl-Friedrich KowalewskiDepartment of General, Visceral, and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Carly R GarrowDepartment of General, Visceral, and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Mona W SchmidtDepartment of General, Visceral, and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Laura BennerDepartment of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany.
Beat P Müller-StichDepartment of General, Visceral, and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Felix NickelDepartment of General, Visceral, and Transplantation Surgery, University of Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany. felix.nickel@med.uni-heidelberg.de.ORCID 0000-0001-6066-8238
Heidelberg University · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe most common way of assessing surgical performance is by expert raters to view a surgical task and rate a trainee's performance. However, there is huge potential for automated skill assessment and workflow analysis using modern technology. The aim of the present study was to evaluate machine learning (ML) algorithms using the data of a Myo armband as a sensor device for skills level assessment and phase detection in laparoscopic training. MATERIALS AND

methodsParticipants of three experience levels in laparoscopy performed a suturing and knot tying task on silicon models. Experts rated performance using Objective Structured Assessment of Surgical Skills (OSATS). Participants wore Myo armbands (Thalmic Labs™, Ontario, Canada) to record acceleration, angular velocity, orientation, and Euler orientation. ML algorithms (decision forest, neural networks, boosted decision tree) were compared for skill level assessment and phase detection.

results28 participants (8 beginner, 10 intermediate, 10 expert) were included, and 99 knots were available for analysis. A neural network regression model had the lowest mean absolute error in predicting OSATS score (3.7 ± 0.6 points, r

conclusionModern machine learning algorithms aid in interpreting complex surgical motion data, even when standard analysis fails. Dynamic time warping offers the potential to process and compare surgical motion data in order to allow automated surgical workflow detection. However, further research is needed to interpret and standardize available data and improve sensor accuracy.

Indexed as

AlgorithmsMachine LearningWorkflowClinical CompetenceDecision TreesHumansLaparoscopyModels, AnatomicNeural Networks, ComputerOntarioSiliconSuture TechniquesSiliconArtificial intelligenceElectromyographyLaparoscopic trainingLaparoscopyMachine learningMyo armbandNeural networksSkill assessmentSurgical educationWorkflow analysis

Identifiers

PMID30790048
OpenAlexW2915423573

What OpenQuestion holds

Textmetadata
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.