Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot Learning.
Biomed Environ Sci
; 36(5): 431-440, 2023 May 20.
Article
em En
| MEDLINE
| ID: mdl-37253669
ABSTRACT
Objective:
To develop a few-shot learning (FSL) approach for classifying optical coherence tomography (OCT) images in patients with inherited retinal disorders (IRDs).Methods:
In this study, an FSL model based on a student-teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control samples.Results:
The FSL model achieved a total accuracy of 0.974-0.983, total sensitivity of 0.934-0.957, total specificity of 0.984-0.990, and total F1 score of 0.935-0.957, which were superior to the total accuracy of the baseline model of 0.943-0.954, total sensitivity of 0.866-0.886, total specificity of 0.962-0.971, and total F1 score of 0.859-0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves (AUC) of the receiver operating characteristic (ROC) curves in most subclassifications.Conclusion:
This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Doenças Retinianas
/
Aprendizado Profundo
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article