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Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice by Mursch-Edlmayr, Anna S.; Ng, Wai Siene; Diniz-Filho, Alberto; Sousa, David C.; Arnold, Louis; Schlenker, Matthew B.; Duenas-Angeles, Karla; Keane, Pearse A.; Crowston, Jonathan G.; Jayaram, Hari is a Medicine article available to read on EtoBox.
What is Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice about?
## Purpose This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression. ## Methods Nonsystematic literature review using the search combinations “Artificial Intelligence,” “Deep Learning,” “Machine Learning,” “Neural Networks,” “Bayesian Networks,” “Glaucoma Diagnosis,” and “Glaucoma Progression.” Information on sensitivity and specificity regarding glaucoma diagnosis and progression analysis as well as methodological details were extracted. ## Results Numerous AI strategies provide promising levels of specificity and sensitivity for structural (e.g. optical coherence tomography [OCT] imaging, fundus photography) and functional (visual field [VF] testing) test modalities used for the detection of glaucoma. Area under receiver operating curve (AROC) values of > 0.90 were achieved with every modality. Combining structural and functional inputs has been shown to even more improve the diagnostic ability. Regarding glaucoma progression, AI strategies can detect progression earlier than conventional methods or potentially from one single VF test. ## Conclusions AI a
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- Author
- Mursch-Edlmayr, Anna S.; Ng, Wai Siene; Diniz-Filho, Alberto; Sousa, David C.; Arnold, Louis; Schlenker, Matthew B.; Duenas-Angeles, Karla; Keane, Pearse A.; Crowston, Jonathan G.; Jayaram, Hari
- Publisher
- Association for Research in Vision and Ophthalmology (ARVO); Association for Research in Vision and Ophthalmology Inc. (ISSN 2164-2591)
- Published
- 2020
- Language
- EN
- Field
- Medicine (Health Sciences)