Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning.

Kato, Naoko; Masumoto, Hiroki; Tanabe, Mao; Sakai, Chikako; Negishi, Kazuno; Torii, Hidemasa; Tabuchi, Hitoshi; Tsubota, Kazuo

Kato, Naoko; Masumoto, Hiroki; Tanabe, Mao; Sakai, Chikako; Negishi, Kazuno; Torii, Hidemasa; Tabuchi, Hitoshi; Tsubota, Kazuo.

Afiliación

Kato N; Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan.
Masumoto H; Department of Technology and Design Thinking for Medicine, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Tanabe M; Department of Technology and Design Thinking for Medicine, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Sakai C; Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan.
Negishi K; Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan.
Torii H; Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan.
Tabuchi H; Department of Technology and Design Thinking for Medicine, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Tsubota K; Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan.

J Clin Med ; 10(4)2021 Feb 18.

Article en En | MEDLINE | ID: mdl-33670732

Palabras clave

corneal crosslinking; deep learning; keratoconus; patients' age; prediction; progression; tomography

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Clin Med Año: 2021 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Suiza

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