A deep learning approach to predict visual field using optical coherence tomography.
PLoS One
; 15(7): e0234902, 2020.
Article
em En
| MEDLINE
| ID: mdl-32628672
ABSTRACT
We developed a deep learning architecture based on Inception V3 to predict visual field using optical coherence tomography (OCT) imaging and evaluated its performance. Two OCT images, macular ganglion cell-inner plexiform layer (mGCIPL) and peripapillary retinal nerve fibre layer (pRNFL) thicknesses, were acquired and combined. A convolutional neural network architecture was constructed to predict visual field using this combined OCT image. The root mean square error (RMSE) between the actual and predicted visual fields was calculated to evaluate the performance. Globally (the entire visual field area), the RMSE for all patients was 4.79 ± 2.56 dB, with 3.27 dB and 5.27 dB for the normal and glaucoma groups, respectively. The RMSE of the macular region (4.40 dB) was higher than that of the peripheral region (4.29 dB) for all subjects. In normal subjects, the RMSE of the macular region (2.45 dB) was significantly lower than that of the peripheral region (3.11 dB), whereas in glaucoma subjects, the RMSE was higher (5.62 dB versus 5.03 dB, respectively). The deep learning method effectively predicted the visual field 24-2 using the combined OCT image. This method may help clinicians determine visual fields, particularly for patients who are unable to undergo a physical visual field exam.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tomografia de Coerência Óptica
/
Testes de Campo Visual
/
Previsões
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Aged
/
Aged80
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Female
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Humans
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Male
/
Middle aged
Idioma:
En
Revista:
PLoS One
Assunto da revista:
CIENCIA
/
MEDICINA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Coréia do Sul