Analysis of Multifocal Visual Evoked Potentials Using Artificial Intelligence Algorithms.
Transl Vis Sci Technol
; 11(1): 10, 2022 01 03.
Article
em En
| MEDLINE
| ID: mdl-35006263
ABSTRACT
Purpose:
Clinical trials for remyelination in multiple sclerosis (MS) require an imaging biomarker. The multifocal visual evoked potential (mfVEP) is an accurate technique for measuring axonal conduction; however, it produces large datasets requiring lengthy analysis by human experts to detect measurable responses versus noisy traces. This study aimed to develop a machine-learning approach for the identification of true responses versus noisy traces and the detection of latency peaks in measurable signals.Methods:
We obtained 2240 mfVEP traces from 10 MS patients using the VS-1 mfVEP machine, and they were classified by a skilled expert twice with an interval of 1 week. Of these, 2025 (90%) were classified consistently and used for the study. ResNet-50 and VGG16 models were trained and tested to produce three outputs no signal, up-sloped signal, or down-sloped signal. Each model ran 1000 iterations with a stochastic gradient descent optimizer with a learning rate of 0.0001.Results:
ResNet-50 and VGG16 had false-positive rates of 1.7% and 0.6%, respectively, when the testing dataset was analyzed (n = 612). The false-negative rates were 8.2% and 6.5%, respectively, against the same dataset. The latency measurements in the validation and testing cohorts in the study were similar.Conclusions:
Our models efficiently analyze mfVEPs with <2% false positives compared with human false positives of <8%. Translational Relevance mfVEP, a safe neurophysiological technique, analyzed using artificial intelligence, can serve as an efficient biomarker in MS clinical trials and signal latency measurement.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Potenciais Evocados Visuais
/
Esclerose Múltipla
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Transl Vis Sci Technol
Ano de publicação:
2022
Tipo de documento:
Article