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Erschienen in: Graefe's Archive for Clinical and Experimental Ophthalmology 1/2018

10.11.2017 | Retinal Disorders

OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications

verfasst von: Philipp Prahs, Viola Radeck, Christian Mayer, Yordan Cvetkov, Nadezhda Cvetkova, Horst Helbig, David Märker

Erschienen in: Graefe's Archive for Clinical and Experimental Ophthalmology | Ausgabe 1/2018

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Abstract

Purpose

Intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF) medications have become the standard of care for their respective indications. Optical coherence tomography (OCT) scans of the central retina provide detailed anatomical data and are widely used by clinicians in the decision-making process of anti-VEGF indication. In recent years, significant progress has been made in artificial intelligence and computer vision research. We trained a deep convolutional artificial neural network to predict treatment indication based on central retinal OCT scans without human intervention.

Method

A total of 183,402 retinal OCT B-scans acquired between 2008 and 2016 were exported from the institutional image archive of a university hospital. OCT images were cross-referenced with the electronic institutional intravitreal injection records. OCT images with a following intravitreal injection during the first 21 days after image acquisition were assigned into the ‘injection’ group, while the same amount of random OCT images without intravitreal injections was labeled as ‘no injection’. After image preprocessing, OCT images were split in a 9:1 ratio to training and test datasets. We trained a GoogLeNet inception deep convolutional neural network and assessed its performance on the validation dataset. We calculated prediction accuracy, sensitivity, specificity, and receiver operating characteristics.

Results

The deep convolutional neural network was successfully trained on the extracted clinical data. The trained neural network classifier reached a prediction accuracy of 95.5% on the images in the validation dataset. For single retinal B-scans in the validation dataset, a sensitivity of 90.1% and a specificity of 96.2% were achieved. The area under the receiver operating characteristic curve was 0.968 on a per B-scan image basis, and 0.988 by averaging over six B-scans per examination on the validation dataset.

Conclusion

Deep artificial neural networks show impressive performance on classification of retinal OCT scans. After training on historical clinical data, machine learning methods can offer the clinician support in the decision-making process. Care should be taken not to mistake neural network output as treatment recommendation and to ensure a final thorough evaluation by the treating physician.
Literatur
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Zurück zum Zitat Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in Neural Information Processing Systems 25 (NIPS 2012). MIT Press, Cambridge MA, pp 1097–1105 Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in Neural Information Processing Systems 25 (NIPS 2012). MIT Press, Cambridge MA, pp 1097–1105
Metadaten
Titel
OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications
verfasst von
Philipp Prahs
Viola Radeck
Christian Mayer
Yordan Cvetkov
Nadezhda Cvetkova
Horst Helbig
David Märker
Publikationsdatum
10.11.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Graefe's Archive for Clinical and Experimental Ophthalmology / Ausgabe 1/2018
Print ISSN: 0721-832X
Elektronische ISSN: 1435-702X
DOI
https://doi.org/10.1007/s00417-017-3839-y

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