With a deep learning-based approach using TensorFlow™, it is possible to detect AMD in SD-OCT with high sensitivity and specificity. With more image data, an expansion of this classifier for other macular diseases or further details in AMD is possible, suggesting an application for this model as a support in clinical decisions. Another possible future application would involve the individual prediction of the progress and success of therapy for different diseases by automatically detecting hidden image information.
Prior to both qualitative and quantitative analysis, OCT-A images must be carefully reviewed as motion artifacts and segmentation errors in current OCT-A technology are frequent particularly in pathologically altered maculae.
In patients with AMD, active ET technology offers an improved image quality in OCT-A imaging regarding presence of motion artifacts at the expense of higher acquisition time.
The choriocapillaris (CC) represents a fundamentally important vascular layer that is subject to physiologic changes with increasing age and that is also associated with a wide range of chorioretinal diseases. So far, information on blood flow in this specific layer has remained limited. With the advent of optical coherence tomography angiography (OCTA), new perspectives and possibilities of CC imaging have begun to evolve. This article shall review the opportunities and challenges of applying OCTA technology to the CC layer and summarize the current clinical efforts in OCTA CC imaging exemplarily in dry age-related macular degeneration and central serous chorioretinopathy.
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