Artificial intelligence in retina.

By Ursula Schmidt-Erfurth, Amir Sadeghipour, Bianca S. Gerendas, Sebastian M. Waldstein, Hrvoje Bogunović. Progress in retinal and eye research, 2018

674 citations recorded by the source corpus.

Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML. The methods of AI-based retinal analyses are diverse and differ widely in their applicability, interpretability and reliability in different datasets and diseases. Fully automated AI-based systems have recently been approved for screening of diabetic retinopathy (DR). The overall potential of ML/DL includes screening, diagnostic grading as well as guidance of therapy with automated detection of disease activity, recurrences, quantification of therapeutic effects and identification of relevant targets for novel therapeutic approaches. Prediction and prognostic conclusions further expand the potential benefit of AI in retina which will enable personalized health care as well as large scale management and will empower the ophthalmologist to provide high quality diagnosis/therapy and successfully deal with the complexity of 21st century ophthalmology.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/54accfee7af5cd4736faa4ab4627ee9bf1edc812

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In Computer Vision and Pattern Recognition

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Citations in the corpus

REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs (2019, positive): “Color fundus photography (CFP, Figure 1) is currently the most economical, non-invasive imaging modality for inspecting the retina (Abràmoff et al., 2010; Schmidt-Erfurth et al., 2018).”
Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “Example architectures include the CNN structures in [33] and [34] where they have employed supervised learning to identify anomalies in retina images and for automated classification of skin lesions, respectively.”
The impact of artificial intelligence in medicine on the future role of the physician (2019, background): “This allows for end-to-end training because a DNN learns to recognize an output category from the input signal directly (Schmidt-Erfurth et al., 2018).”