End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning.
Graefes Arch Clin Exp Ophthalmol
; 260(5): 1663-1673, 2022 May.
Article
en En
| MEDLINE
| ID: mdl-35066704
ABSTRACT
PURPOSE:
To develop and validate a deep learning system for diabetic retinopathy (DR) grading based on fundus fluorescein angiography (FFA) images.METHODS:
A total of 11,214 FFA images from 705 patients were collected to form the internal dataset. Three convolutional neural networks, namely VGG16, RestNet50, and DenseNet, were trained using a nine-square grid input, and heat maps were generated. Subsequently, a comparison between human graders and the algorithm was performed. Lastly, the best model was tested on two external datasets (Xian dataset and Ningbo dataset).RESULTS:
VGG16 performed the best, with a maximum accuracy of 94.17%, and had an AUC of 0.972, 0.922, and 0.994 for levels 1, 2, and 3, respectively. For Xian dataset, our model reached the accuracy of 82.47% and AUC of 0.910, 0.888, and 0.976 for levels 1, 2, and 3. As for Ningbo dataset, the network performed with the accuracy of 88.89% and AUC of 0.972, 0.756, and 0.945 for levels 1, 2, and 3.CONCLUSIONS:
A deep learning system for DR staging was trained based on FFA images and evaluated through human-machine comparisons as well as external dataset testing. The proposed system will help clinical practitioners to diagnose and treat DR patients, and lay a foundation for future applications of other ophthalmic or general diseases.Palabras clave
Texto completo:
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Bases de datos:
MEDLINE
Asunto principal:
Diabetes Mellitus
/
Retinopatía Diabética
/
Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Graefes Arch Clin Exp Ophthalmol
Año:
2022
Tipo del documento:
Article
País de afiliación:
China