Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study.

Journal: Eye (London, England)

PMID: 31267086

Abstract

OBJECTIVES: To evaluate the performance of a deep learning based Artificial Intelligence (AI) software for detection of glaucoma from stereoscopic optic disc photographs, and to compare this performance to the performance of a large cohort of ophthalmologists and optometrists.

Authors

Thomas W Rogers

Department of Computer Science, University College London, London, UK.
Nicolas Jaccard

Department of Computer Science, University College London, London, UK.
Francis Carbonaro

Mater Dei Hospital, Triq Dun Karm, L'Imsida, Malta.
Hans G Lemij

Rotterdam Eye Hospital, Rotterdam, The Netherlands.
Koenraad A Vermeer

Rotterdam Eye Hospital, Rotterdam, The Netherlands.
Nicolaas J Reus

Rotterdam Eye Hospital, Rotterdam, The Netherlands.
Sameer Trikha

Visulytix Ltd, Screenworks, Highbury Grove, Highbury East, London, N5 2EF, UK.

Keywords

Artificial Intelligence Clinical Competence Deep Learning Diagnosis, Computer-Assisted Europe False Positive Reactions Glaucoma, Open-Angle Humans Observer Variation Ophthalmologists Optic Disk Optic Nerve Diseases Optometrists Photography Predictive Value of Tests Reproducibility of Results Retrospective Studies ROC Curve Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (31267086)

Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study.

Abstract

Authors

Keywords

External Resources

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