Evidence map›Paper›PMID 39755835›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

Toward structured abdominal examination training using augmented reality.

Lovis Schwenderling, Laura Isabel Hanke, Undine Holst, Florentine Huettl, Fabian Joeres, Tobias Huber, Christian Hansen

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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. 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

7 authors.

Lovis Schwenderling *Faculty of Computer Science and Research Campus STIMULATE, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.ORCID http://orcid.org/0000-0003-4538-2201
Laura Isabel Hanke *Clinic for General, Visceral and Transplant Surgery, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.ORCID http://orcid.org/0000-0001-8483-1984
Undine HolstFaculty of Computer Science and Research Campus STIMULATE, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.ORCID http://orcid.org/0009-0004-2068-8946
Florentine HuettlClinic for General, Visceral and Transplant Surgery, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.ORCID http://orcid.org/0000-0002-3281-8566
Fabian JoeresFaculty of Computer Science and Research Campus STIMULATE, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.ORCID http://orcid.org/0000-0001-8105-9263
Tobias HuberClinic for General, Visceral and Transplant Surgery, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.ORCID http://orcid.org/0000-0003-3219-501X
Christian HansenFaculty of Computer Science and Research Campus STIMULATE, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany. hansen@isg.cs.uni-magdeburg.de.ORCID http://orcid.org/0000-0002-5734-7529

Funding

Bundesministerium für Wirtschaft und Klimaschutz 16KN093944Deutsche Forschungsgemeinschaft HA 7819/1-2
6 · The paper itself

Abstract

purposeStructured abdominal examination is an essential part of the medical curriculum and surgical training, requiring a blend of theory and practice from trainees. Current training methods, however, often do not provide adequate engagement, fail to address individual learning needs or do not cover rare diseases.

methodsIn this work, an application for structured Abdominal Examination Training using Augmented Reality (AETAR) is presented. Required theoretical knowledge is displayed step by step via virtual indicators directly on the associated body regions. Exercises facilitate building up the routine in performing the examination. AETAR was evaluated in an exploratory user study with medical students (n=12) and teaching surgeons (n=2).

resultsLearning with AETAR was described as fun and beneficial. Usability (SUS=73) and rated suitability for teaching were promising. All students improved in a knowledge test and felt more confident with the abdominal examination. Shortcomings were identified in the area of interaction, especially in teaching examination-specific movements.

conclusionAETAR represents a first approach to structured abdominal examination training using augmented reality. The application demonstrates the potential to improve educational outcomes for medical students and provides an important foundation for future research and development in digital medical education.

Indexed as

AbdomenAugmented RealityPhysical ExaminationAdultClinical CompetenceCurriculumEducational MeasurementEducation, Medical, UndergraduateFemaleHumansMaleStudents, MedicalAugmented realityHuman–computer interactionMedical educationMixed realitySurgical trainingVisualization

Identifiers

PMID39755835
PMCPMC12055959

What OpenQuestion holds

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LicenceCC BY
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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.