Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device.

Diao, Tina; Kushzad, Fareshta; Patel, Megh D; Bindiganavale, Megha P; Wasi, Munam; Kochenderfer, Mykel J; Moss, Heather E

Diao, Tina; Kushzad, Fareshta; Patel, Megh D; Bindiganavale, Megha P; Wasi, Munam; Kochenderfer, Mykel J; Moss, Heather E.

Afiliación

Diao T; Department of Management Science & Engineering, Stanford University, Stanford, CA, United States.
Kushzad F; Department of Ophthalmology, Stanford University, Palo Alto, CA, United States.
Patel MD; Department of Ophthalmology, Stanford University, Palo Alto, CA, United States.
Bindiganavale MP; Department of Ophthalmology, Stanford University, Palo Alto, CA, United States.
Wasi M; Department of Ophthalmology, Stanford University, Palo Alto, CA, United States.
Kochenderfer MJ; Department of Ophthalmology, Stanford University, Palo Alto, CA, United States.
Moss HE; Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, United States.

Front Med (Lausanne) ; 8: 771713, 2021.

Article en En | MEDLINE | ID: mdl-34926514

Palabras clave

classification; electroretinogram (ERG); machine learning; optic neuropathy; photopic negative response (PhNR); time series analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Front Med (Lausanne) Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza

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