Evidence map›Paper›PMID 39980861›Full record

ArticleFrontiers in bioengineering and biotechnology2025

Non-contact tracking of shoulder bones using ultrasound and stereophotogrammetry.

Ahmed Sewify, Maxence Lavaill, Dermot O'Rourke, Maria Antico, Peter Pivonka, Davide Fontanarosa, Saulo Martelli

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. 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

7 authors.

Ahmed SewifySchool of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, Brisbane, QLD, Australia.
Maxence LavaillCentre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia.
Dermot O'RourkeCentre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia.
Maria AnticoSchool of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, Brisbane, QLD, Australia.
Peter PivonkaCentre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia.
Davide FontanarosaSchool of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, Brisbane, QLD, Australia.
Saulo MartelliCentre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: We explored the integration of 3D ultrasound (US) imaging with motion capture technology for non-invasively tracking bones of the shoulder district during normal activity. Our study aimed to demonstrate Method: We registered previously acquired full bone shapes to spatially small bony surface patches segmented from 3D US. The bone registration approach used was based on Results: CPD provided the highest accuracy in the simulation at the cost of 8x longer computation compared to ICP. The RMSE errors were <1 mm for the humerus and scapula at all elevations. Conclusion: In summary, we demonstrated the feasibility and accuracy of tracking shoulder bones with 3D US in a simulation and a cadaveric experiment. We discovered that CPD may be a more suitable registration method for the task than ICP. We also discussed that 3D US with motion capture technology is very promising for dynamic bone tracking, but the US technology may not be ready for the task yet.

Indexed as

3D-ultrasoundbone registrationbone trackingex-vivoiterative closest point (ICP) algorithmShoulderstereophotogrammetryultrasound

Identifiers

PMID39980861
PMCPMC11839626

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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.