| Item type: | Article | ||||
|---|---|---|---|---|---|
| Journal or Publication Title: | Graefe's Archive for Clinical and Experimental Ophthalmology | ||||
| Publisher: | Springer | ||||
| Place of Publication: | NEW YORK | ||||
| Volume: | 256 | ||||
| Number of Issue or Book Chapter: | 1 | ||||
| Page Range: | pp. 91-98 | ||||
| Date: | 2018 | ||||
| Institutions: | Medicine > Lehrstuhl für Augenheilkunde | ||||
| Identification Number: |
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| Keywords: | ; Deep learning; Optical coherence tomography; Age-related macular degeneration; Diabetic retinopathy; Artificial intelligence; Computer vision; Computer-aided diagnosis | ||||
| Dewey Decimal Classification: | 600 Technology > 610 Medical sciences Medicine | ||||
| Status: | Published | ||||
| Refereed: | Yes, this version has been refereed | ||||
| Created at the University of Regensburg: | Yes | ||||
| Item ID: | 47606 |
Web of ScienceAbstract
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 ...
Abstract
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. 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. 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. 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.
Metadata last modified: 28 Jul 2021 17:26
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