Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images.
Ann N Y Acad Sci
; 1497(1): 15-26, 2021 08.
Article
em En
| MEDLINE
| ID: mdl-33638189
ABSTRACT
We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom-built OCT system based on an akinetic swept laser at 1322 nm with a lateral resolution of â¼13 µm and using split-spectrum amplitude decorrelation. Our data set consisted of 24 stitched angiograms of the full ear, with a size of approximately 8.2 × 8.2 mm, evenly distributed between healthy and diabetic mice. The deep learning classification algorithm uses the ResNet v2 convolutional neural network architecture and was trained on small patches extracted from the full ear angiograms. For individual patches, we obtained a cross-validated accuracy of 0.925 and an area under the receiver operating characteristic curve (ROC AUC) of 0.974. Averaging over multiple patches extracted from each ear resulted in the correct classification of all 24 ears.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Angiografia
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Tomografia de Coerência Óptica
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Diabetes Mellitus Experimental
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Orelha
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Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article