GLAUCOMA: Edited by Donald L. BudenzArtificial intelligence and complex statistical modeling in glaucoma diagnosis and managementSalazar, Humbertoa; Misra, Vedantb; Swaminathan, Swarup S.aAuthor Information aBascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL bOpenAI, San Francisco, California, USA Correspondence to Swarup S. Swaminathan, MD, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, 900 NW 17th Street, Miami, Florida, USA. E-mail: [email protected] Current Opinion in Ophthalmology: March 2021 - Volume 32 - Issue 2 - p 105-117 doi: 10.1097/ICU.0000000000000741 Buy Metrics Abstract Purpose of review The field of artificial intelligence has grown exponentially in recent years with new technology, methods, and applications emerging at a rapid rate. Many of these advancements have been used to improve the diagnosis and management of glaucoma. We aim to provide an overview of recent publications regarding the use of artificial intelligence to enhance the detection and treatment of glaucoma. Recent findings Machine learning classifiers and deep learning algorithms have been developed to autonomously detect early structural and functional changes of glaucoma using different imaging and testing modalities such as fundus photography, optical coherence tomography, and standard automated perimetry. Artificial intelligence has also been used to further delineate structure-function correlation in glaucoma. Additional ‘structure-structure’ predictions have been successfully estimated. Other machine learning techniques utilizing complex statistical modeling have been used to detect glaucoma progression, as well as to predict future progression. Although not yet approved for clinical use, these artificial intelligence techniques have the potential to significantly improve glaucoma diagnosis and management. Summary Rapidly emerging artificial intelligence algorithms have been used for the detection and management of glaucoma. These algorithms may aid the clinician in caring for patients with this complex disease. Further validation is required prior to employing these techniques widely in clinical practice. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.