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Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners.
Uncini, Antonino; Aretusi, Graziano; Manganelli, Fiore; Sekiguchi, Yukari; Magy, Laurent; Tozza, Stefano; Tsuneyama, Atsuko; Lefour, Sophie; Kuwabara, Satoshi; Santoro, Lucio; Ippoliti, Luigi.
Afiliación
  • Uncini A; Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio", Via Luigi Polacchi 11, 66100, Chieti-Pescara, Italy. uncini@unich.it.
  • Aretusi G; Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio", Via Luigi Polacchi 11, 66100, Chieti-Pescara, Italy.
  • Manganelli F; Statistics Unit, Department of Economics, University "G. d'Annunzio", Chieti-Pescara, Italy.
  • Sekiguchi Y; Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples Federico II, Naples, Italy.
  • Magy L; Department of Neurology, Graduate School of Medicine, Chiba University, Chiba, Japan.
  • Tozza S; National Reference Centre for Rare Peripheral Neuropathies and Department of Neurology, University of Limoges, Limoges, France.
  • Tsuneyama A; Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples Federico II, Naples, Italy.
  • Lefour S; Department of Neurology, Graduate School of Medicine, Chiba University, Chiba, Japan.
  • Kuwabara S; National Reference Centre for Rare Peripheral Neuropathies and Department of Neurology, University of Limoges, Limoges, France.
  • Santoro L; Department of Neurology, Graduate School of Medicine, Chiba University, Chiba, Japan.
  • Ippoliti L; Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples Federico II, Naples, Italy.
Neurol Sci ; 41(12): 3719-3727, 2020 Dec.
Article en En | MEDLINE | ID: mdl-32518996
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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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Polineuropatías / Enfermedad de Charcot-Marie-Tooth / Polirradiculoneuropatía Crónica Inflamatoria Desmielinizante Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Neurol Sci Asunto de la revista: NEUROLOGIA Año: 2020 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Polineuropatías / Enfermedad de Charcot-Marie-Tooth / Polirradiculoneuropatía Crónica Inflamatoria Desmielinizante Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Neurol Sci Asunto de la revista: NEUROLOGIA Año: 2020 Tipo del documento: Article País de afiliación: Italia