Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners
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ORIGINAL ARTICLE
Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners Antonino Uncini 1 & Graziano Aretusi 1,2 & Fiore Manganelli 3 & Yukari Sekiguchi 4 & Laurent Magy 5 & Stefano Tozza 3 & Atsuko Tsuneyama 4 & Sophie Lefour 5 & Satoshi Kuwabara 4 & Lucio Santoro 3 & Luigi Ippoliti 2 Received: 16 March 2020 / Accepted: 30 May 2020 # Fondazione Società Italiana di Neurologia 2020
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. Keywords Polyneuropathies . Electrodiagnosis . Diagnostic accuracy . Supervised learning algorithms
Introduction Antonino Uncini and Graziano Aretusi contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10072-020-04499-y) contains supplementary material, which is available to authorized users. * Antonino Uncini [email protected] 1
Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio”, Via Luigi Polacchi 11, 66100 Chieti-Pescara, Italy
2
Statistics Unit, Department of Economics, University “G. d’Annunzio”, Chieti-Pescara, Italy
3
Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples Federico II, Naples, Italy
4
Department of Neurology, Graduate School of Medicine, Chiba University, Chiba, Japan
5
National Reference Centre for Rare Peripheral Neuropathies and Department of Neurology, University of Limoges, Limoges, France
Polyneuropathy is a common neurological disorder. The prevalence is 2.4% in the overall population, rising to 8% in subjects older than 55 [1]. There are over 200 identified causes of neuropat
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