Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images

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 ∼13 μm and us...

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Bibliographic Details
Main Authors: Gröschl, M. (Author), Hohenadl, C. (Author), Pfister, M. (Author), Schäfer, B.J (Author), Schmetterer, L. (Author), Schützenberger, K. (Author), Stegmann, H. (Author), Werkmeister, R.M (Author)
Format: Article
Language:English
Published: John Wiley and Sons Inc 2021
Subjects:
Online Access:View Fulltext in Publisher
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001 10.1111-nyas.14582
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020 |a 00778923 (ISSN) 
245 1 0 |a Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images 
260 0 |b John Wiley and Sons Inc  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1111/nyas.14582 
520 3 |a 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 ∼13 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. © 2021 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals LLC on behalf of New York Academy of Sciences. 
650 0 4 |a angiographic imaging 
650 0 4 |a diabetes 
650 0 4 |a machine learning 
650 0 4 |a optical coherence tomography 
700 1 |a Gröschl, M.  |e author 
700 1 |a Hohenadl, C.  |e author 
700 1 |a Pfister, M.  |e author 
700 1 |a Schäfer, B.J.  |e author 
700 1 |a Schmetterer, L.  |e author 
700 1 |a Schützenberger, K.  |e author 
700 1 |a Stegmann, H.  |e author 
700 1 |a Werkmeister, R.M.  |e author