Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy.
Deep convolutional neural network
Deep learning
Proliferative diabetic retinopathy
Ultrawide-field fundus ophthalmoscopy
Journal
International ophthalmology
ISSN: 1573-2630
Titre abrégé: Int Ophthalmol
Pays: Netherlands
ID NLM: 7904294
Informations de publication
Date de publication:
Oct 2019
Oct 2019
Historique:
received:
28
06
2018
accepted:
14
01
2019
pubmed:
25
2
2019
medline:
18
1
2020
entrez:
25
2
2019
Statut:
ppublish
Résumé
We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, to detect treatment-naïve proliferative diabetic retinopathy (PDR). We conducted training with the DCNN using 378 photographic images (132 PDR and 246 non-PDR) and constructed a deep learning model. The area under the curve (AUC), sensitivity, and specificity were examined. The constructed deep learning model demonstrated a high sensitivity of 94.7% and a high specificity of 97.2%, with an AUC of 0.969. Our findings suggested that PDR could be diagnosed using wide-angle camera images and deep learning.
Identifiants
pubmed: 30798455
doi: 10.1007/s10792-019-01074-z
pii: 10.1007/s10792-019-01074-z
doi:
Types de publication
Letter
Langues
eng
Sous-ensembles de citation
IM
Pagination
2153-2159Références
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