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Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners

Neurological Sciences volume 41, pages 3719–3727 (2020)Cite this article

Abstract

Objective

The interpretation of electrophysiological findings may lead to misdiagnosis in polyneuropathies. We investigated the electrodiagnostic accuracy of three supervised learning algorithms (SLAs): shrinkage discriminant analysis, multinomial logistic regression, and support vector machine (SVM), and three expert and three trainee neurophysiologists.

Methods

We enrolled 434 subjects with the following diagnoses: chronic inflammatory demyelinating polyneuropathy (99), Charcot-Marie-Tooth disease type 1A (124), hereditary neuropathy with liability to pressure palsy (46), diabetic polyneuropathy (67), and controls (98). In each diagnostic class, 90% of subjects were used as training set for SLAs to establish the best performing SLA by tenfold cross validation procedure and 10% of subjects were employed as test set. Performance indicators were accuracy, precision, sensitivity, and specificity.

Results

SVM showed the highest overall diagnostic accuracy both in training and test sets (90.5 and 93.2%) and ranked first in a multidimensional comparison analysis. Overall accuracy of neurophysiologists ranged from 54.5 to 81.8%.

Conclusions

This proof of principle study shows that SVM provides a high electrodiagnostic accuracy in polyneuropathies. We suggest that the use of SLAs in electrodiagnosis should be exploited to possibly provide a diagnostic support system especially helpful for the less experienced practitioners.

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Data Availability

Deidentified participant data, data dictionary and R script for the implementation of the statistical analysis will be available on request.

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Author information

Author notes

  1. Antonino Uncini and Graziano Aretusi contributed equally to this work.

Authors and Affiliations

  1. Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio”, Via Luigi Polacchi 11, 66100, Chieti-Pescara, Italy

    Antonino Uncini & Graziano Aretusi

  2. Statistics Unit, Department of Economics, University “G. d’Annunzio”, Chieti-Pescara, Italy

    Graziano Aretusi & Luigi Ippoliti

  3. Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples Federico II, Naples, Italy

    Fiore Manganelli, Stefano Tozza & Lucio Santoro

  4. Department of Neurology, Graduate School of Medicine, Chiba University, Chiba, Japan

    Yukari Sekiguchi, Atsuko Tsuneyama & Satoshi Kuwabara

  5. National Reference Centre for Rare Peripheral Neuropathies and Department of Neurology, University of Limoges, Limoges, France

    Laurent Magy & Sophie Lefour

Authors
  1. Antonino Uncini
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  2. Graziano Aretusi
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  3. Fiore Manganelli
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  4. Yukari Sekiguchi
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  5. Laurent Magy
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  6. Stefano Tozza
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  7. Atsuko Tsuneyama
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  8. Sophie Lefour
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  9. Satoshi Kuwabara
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  10. Lucio Santoro
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  11. Luigi Ippoliti
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Contributions

AU and GA equally contributed to the study. AU, GA, and LI designed the study and AU acted as study supervisor. FM, YS, LM, ST, AT, SK, and LS collected the data. GA made the statistical analysis. AU, GA, and LI analyzed and interpreted the results. All the authors contributed to drafting and revising the manuscript and gave their approval to the final version of the manuscript.

Corresponding author

Correspondence to Antonino Uncini.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

The study was carried out in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University Federico II of Naples (320/17) and the institutional Medical Ethics Research Committee of each center.

Consent to participate

Not applicable.

Consent for publication

All subjects signed a written informed consent that allowed the utilization of electrophysiological data for research purposes.

Informed consent

All subjects signed a written informed consent that allowed the utilization of electrophysiological data for research purposes.

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Not applicable (software application or custom code).

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Uncini, A., Aretusi, G., Manganelli, F. et al. Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners. Neurol Sci 41, 3719–3727 (2020). https://doi.org/10.1007/s10072-020-04499-y

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