Using machine learning algorithms to enhance the diagnostic performance of electrical impedance myography
Introduction/Aims We assessed the classification performance of machine learning (ML) using multifrequency electrical impedance myography (EIM) values to improve upon diagnostic outcomes as compared to those based on a single EIM value. Methods EIM data was obtained from unilateral excised gastrocne...
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Published in | Muscle & nerve Vol. 66; no. 3; pp. 354 - 361 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken, USA
John Wiley & Sons, Inc
01.09.2022
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Introduction/Aims
We assessed the classification performance of machine learning (ML) using multifrequency electrical impedance myography (EIM) values to improve upon diagnostic outcomes as compared to those based on a single EIM value.
Methods
EIM data was obtained from unilateral excised gastrocnemius in eighty diseased mice (26 D2‐mdx, Duchenne muscular dystrophy model, 39 SOD1G93A ALS model, and 15 db/db, a model of obesity‐induced muscle atrophy) and 33 wild‐type (WT) animals. We assessed the classification performance of a ML random forest algorithm incorporating all the data (multifrequency resistance, reactance and phase values) comparing it to the 50 kHz phase value alone.
Results
ML outperformed the 50 kHz analysis as based on receiver‐operating characteristic curves and measurement of the area under the curve (AUC). For example, comparing all diseases together versus WT from the test set outputs, the AUC was 0.52 for 50 kHz phase, but was 0.94 for the ML model. Similarly, when comparing ALS versus WT, the AUCs were 0.79 for 50 kHz phase and 0.99 for ML.
Discussion
Multifrequency EIM using ML improves upon classification compared to that achieved with a single‐frequency value. ML approaches should be considered in all future basic and clinical diagnostic applications of EIM. |
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Bibliography: | Funding information National Institute of Neurological Disorders and Stroke, Grant/Award Numbers: R01NS055099, R01NS091159; National Institutes of Health, Grant/Award Numbers: R01NS055099, R01NS099159 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0148-639X 1097-4598 |
DOI: | 10.1002/mus.27664 |