Evidence map›Paper›PMID 42017436›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Flexible IMU Sensor Array For 3D Colonoscope Shape Reconstruction and AI-Based Loop Detection.

Tuukka Panula, Anni Halkilahti, Andrei Ivanov, Matti Kaisti

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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

4 authors.

Tuukka PanulaDepartment of computing, University of Turku, Turku, Finland.ORCID https://orcid.org/0000-0003-0706-7542
Anni HalkilahtiDepartment of computing, University of Turku, Turku, Finland.
Andrei IvanovDepartment of computing, University of Turku, Turku, Finland.
Matti KaistiDepartment of computing, University of Turku, Turku, Finland.ORCID https://orcid.org/0000-0001-8118-2851

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Looping during colonoscopy procedures is a major cause of patient discomfort and increases the risk of complications. With the rising incidence of colorectal cancer, there is an urgent need for safer and more efficient colonoscopy techniques. We developed a proof-of-concept platform for 3D visualization of the colonoscope combined with artificial intelligence (AI)-based loop detection. The system consists of an array of 15 inertial measurement units (IMUs) mounted on a flexible printed circuit board that can be retrofitted into the instrument channel of conventional colonoscopes. Using sensor fusion algorithms, the position and orientation of individual IMUs are estimated to reconstruct the 3D shape of the colonoscope in real time. The system successfully reconstructed the colonoscope shape in a silicone colon phantom, and the AI model achieved high loop-detection performance with an area under the receiver operating characteristic curve of 0.95 on test data. These results demonstrate feasibility in an artificial environment, while further validation in clinical settings is required.

Indexed as

Artificial IntelligenceColonoscopesColonoscopyImaging, Three-DimensionalAlgorithmsEquipment DesignHumansIntelligent SystemsPhantoms, Imagingaicolonoscopyimuloopingsensor

Identifiers

PMID42017436
PMCPMC13285112

What OpenQuestion holds

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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.