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Jost Lennart Lauermann, Maximilian Treder, Maged Alnawaiseh, Chrristoph Clemens, Nicole Eter, Florian Alten; Standardization and acceleration of OCT Angiography image quality assessment using a deep learning algorithm. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1499.
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© ARVO (1962-2015); The Authors (2016-present)
To expedite and to standardize the process of image quality assessment in optical coherence tomography angiography (OCTA) using a specialized deep learning algorithm (DLA).
200 randomly chosen en-face macular OCTA images of the central 3x3 mm superficial vascular plexus were evaluated retrospectively by an OCTA experienced reader. Images were defined either as sufficient (group 1, n=100) or insufficient image quality (group 2, n=100) based on Motion Artifact Score (MAS) and Segmentation Accuracy Score (SAS). A pre-trained multi-layer deep convolutional neural network (DCNN) was trained and validated with 160 of these en-face OCTA scans (group 1: 80; group 2: 80). Training accuracy and validation accuracy were computed. The DLA was tested in detecting 40 untrained OCTA images (group 1: 20; group 2: 20). An insufficient image quality probability score (IPS) and a sufficient image quality probability score (SPS) were calculated.
Training and validation accuracy were 97 % and 100 %. 90 % (18/20) of the OCTA images with insufficient image quality and 90 % (18/20) with sufficient image quality were correctly classified by the DLA. Mean IPS was 0.88±0.21, mean SPS was 0.84±0.19. Discrimination between both groups was highly significant (p<0.001). Sensitivity of the DLA was 90 %, specificity 90 % and accuracy 90 %.
Deep learning (DL) appears to be a potential approach to automatically distinguish between sufficient and insufficient OCTA image quality and to establish image quality standards in this recent imaging modality.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.
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