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July 24, 2012; 79 (4) Articles

Machine learning algorithms to classify spinal muscular atrophy subtypes

Tuhin Srivastava, Basil T. Darras, Jim S. Wu, Seward B. Rutkove
First published July 11, 2012, DOI: https://doi.org/10.1212/WNL.0b013e3182604395
Tuhin Srivastava
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Basil T. Darras
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Jim S. Wu
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Machine learning algorithms to classify spinal muscular atrophy subtypes
Tuhin Srivastava, Basil T. Darras, Jim S. Wu, Seward B. Rutkove
Neurology Jul 2012, 79 (4) 358-364; DOI: 10.1212/WNL.0b013e3182604395

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Abstract

Objectives: The development of better biomarkers for disease assessment remains an ongoing effort across the spectrum of neurologic illnesses. One approach for refining biomarkers is based on the concept of machine learning, in which individual, unrelated biomarkers are simultaneously evaluated. In this cross-sectional study, we assess the possibility of using machine learning, incorporating both quantitative muscle ultrasound (QMU) and electrical impedance myography (EIM) data, for classification of muscles affected by spinal muscular atrophy (SMA).

Methods: Twenty-one normal subjects, 15 subjects with SMA type 2, and 10 subjects with SMA type 3 underwent EIM and QMU measurements of unilateral biceps, wrist extensors, quadriceps, and tibialis anterior. EIM and QMU parameters were then applied in combination using a support vector machine (SVM), a type of machine learning, in an attempt to accurately categorize 165 individual muscles.

Results: For all 3 classification problems, normal vs SMA, normal vs SMA 3, and SMA 2 vs SMA 3, use of SVM provided the greatest accuracy in discrimination, surpassing both EIM and QMU individually. For example, the accuracy, as measured by the receiver operating characteristic area under the curve (ROC-AUC) for the SVM discriminating SMA 2 muscles from SMA 3 muscles was 0.928; in comparison, the ROC-AUCs for EIM and QMU parameters alone were only 0.877 (p < 0.05) and 0.627 (p < 0.05), respectively.

Conclusions: Combining EIM and QMU data categorizes individual SMA-affected muscles with very high accuracy. Further investigation of this approach for classifying and for following the progression of neuromuscular illness is warranted.

GLOSSARY

AUC=
area under the curve;
EIM=
electrical impedance myography;
PCA=
principal component analysis;
QMU=
quantitative muscle ultrasound;
ROC=
receiver operating characteristic;
SMA=
spinal muscular atrophy;
SVM=
support vector machine

Footnotes

  • Study funding: Supported by the Spinal Muscular Atrophy Foundation.

  • Received November 2, 2011.
  • Accepted March 19, 2012.
  • Copyright © 2012 by AAN Enterprises, Inc.
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