Deep learning models for screening of high myopia using optical coherence tomography.
Sci Rep
; 11(1): 21663, 2021 11 04.
Article
em En
| MEDLINE
| ID: mdl-34737335
ABSTRACT
This study aimed to validate and evaluate deep learning (DL) models for screening of high myopia using spectral-domain optical coherence tomography (OCT). This retrospective cross-sectional study included 690 eyes in 492 patients with OCT images and axial length measurement. Eyes were divided into three groups based on axial length a "normal group," a "high myopia group," and an "other retinal disease" group. The researchers trained and validated three DL models to classify the three groups based on horizontal and vertical OCT images of the 600 eyes. For evaluation, OCT images of 90 eyes were used. Diagnostic agreements of human doctors and DL models were analyzed. The area under the receiver operating characteristic curve of the three DL models was evaluated. Absolute agreement of retina specialists was 99.11% (range 97.78-100%). Absolute agreement of the DL models with multiple-column model was 100.0% (ResNet 50), 90.0% (Inception V3), and 72.22% (VGG 16). Areas under the receiver operating characteristic curves of the DL models with multiple-column model were 0.99 (ResNet 50), 0.97 (Inception V3), and 0.86 (VGG 16). The DL model based on ResNet 50 showed comparable diagnostic performance with retinal specialists. The DL model using OCT images demonstrated reliable diagnostic performance to identify high myopia.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Tomografia de Coerência Óptica
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Miopia
Tipo de estudo:
Diagnostic_studies
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Observational_studies
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Prevalence_studies
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Prognostic_studies
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Risk_factors_studies
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Screening_studies
Limite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
País/Região como assunto:
Asia
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2021
Tipo de documento:
Article