Evidence map›Paper›PMID 42171878›Full record

ArticleAnnals of biomedical engineering2026

Multifrequency Electrical Impedance Myography Enhanced with Machine Learning for Screening Patients with Neuromuscular Disorders.

Buket Sonbas-Cobb, Seward B Rutkove, Baoguo Wei, Sara Robicheau, W David Arnold, Kaneshia Hives, Amy Bartlett, Peter Riley, Aleah Pagan, Stephen M Chrzanowski and 2 more

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Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

12 authors.

Buket Sonbas-CobbBeth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-7335-0616
Seward B RutkoveBeth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Baoguo WeiMyolex, Inc, Brookline, MA, USA.
Sara RobicheauMyolex, Inc, Brookline, MA, USA.
W David ArnoldDepartment of Physical Medicine and Rehabilitation, University of Missouri, Columbia, MO, USA.
Kaneshia HivesDepartment of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Amy BartlettDepartment of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Peter RileyDepartment of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Aleah PaganDepartment of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Stephen M ChrzanowskiDepartments of Neurology and Pediatrics, UMass Memorial Medical Center, Worcester, MA, USA.
Basil T DarrasDepartment of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Stephen J KolbDepartment of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, USA. Stephen.Kolb@osumc.edu.

Funding

An integrated electrical impedance myography platform for neuromuscular disease classification and diagnosisR44NS113756 · NINDS · MYOLEX, INC. · PI LUPTON, ELMER C, RUTKOVE, SEWARD B. · 2019 to 2020
$1.5M
NINDS NIH HHS R44NS113756
6 · The paper itself

Abstract

purposeWe evaluated surface electrical impedance myography (EIM) enhanced with machine learning to serve as a new office-based screening tool for neuromuscular disease

methodsEIM of nine muscles was successfully performed in 119 adults and 111 children (approximately half healthy and half diseased) for a total of 3158 individual muscle measurements. Multifrequency data were processed with feature engineering and classification. Nested cross-validation assessed performance, with muscle-based predictions aggregated to participant via majority voting

resultsSingle-feature analyses showed moderate-to-good discrimination (area-under-ROC curve of 0.62-0.73). When multifrequency features were used, participant-based logistic regression and extra trees ensemble models achieved 84% accuracy with 88.1% sensitivity in adults and 93% accuracy and 94.6% sensitivity in children. Beyond classification, regression using EIM predicted muscle strength with R

Indexed as

Electrical impedance myography (EIM)Machine learning (ML)Muscle strength predictionNeuromuscular disorders

Identifiers

PMID42171878

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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.