Evidence map›Paper›PMID 38139721›Full record

ArticleSensors (Basel, Switzerland)2023

Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers.

Fatemeh Shomal Zadeh, Ryan G L Koh, Banu Dilek, Kei Masani, Dinesh Kumbhare

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 40% 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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. Review
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

5 authors at 2 institutions in 2 countries.

Fatemeh Shomal ZadehInstitute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.ORCID 0000-0003-2046-8578
Ryan G L KohKITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada.ORCID 0000-0001-8662-1008
Banu DilekDepartment of Physical Medicine and Rehabilitation, Dokuz Eylul University, Izmir 35340, Turkey.
Kei MasaniInstitute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.ORCID 0000-0002-0207-3241
Dinesh KumbhareInstitute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.
University Health Network · CADokuz Eylül University · TR

Funding

Banu Dilek (the third author) was supported by the Tubitak 2219 grant program 1059B192100800
6 · The paper itself

Abstract

Myofascial pain syndrome is a chronic pain disorder characterized by myofascial trigger points (MTrPs). Quantitative ultrasound (US) techniques can be used to discriminate MTrPs from healthy muscle. In this study, 90 B-mode US images of upper trapezius muscles were collected from 63 participants (left and/or right side(s)). Four texture feature approaches (individually and a combination of them) were employed that focused on identifying spots, and edges were used to explore the discrimination between the three groups: active MTrPs (

Indexed as

Chronic PainMyofascial Pain SyndromesSuperficial Back MusclesHumansTrigger PointsUltrasonographymachine learningmyofascial trigger pointtexture featuresultrasound

Identifiers

PMID38139721
PMCPMC10747637
OpenAlexW4389925444

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.