Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions
Roberto Nunez,
Alon Harris,
Omar Ibrahim,
James Keller,
Christopher K. Wikle,
Erin Robinson,
Ryan Zukerman,
Brent Siesky,
Alice Verticchio,
Lucas Rowe,
Giovanna Guidoboni
Affiliations
Roberto Nunez
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
Alon Harris
Department of Ophthalmology, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
Omar Ibrahim
Department of Electrical Engineering, Tikrit University, Tikrit P.O. Box 42, Iraq
James Keller
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
Christopher K. Wikle
Department of Statistic, University of Missouri, Columbia, MO 65211, USA
Erin Robinson
Department of Social Work, University of Missouri, Columbia, MO 65211, USA
Ryan Zukerman
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York-Presbyterian Hospital, New York, NY 10034, USA
Brent Siesky
Department of Ophthalmology, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
Alice Verticchio
Department of Ophthalmology, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
Lucas Rowe
Department of Ophthalmology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
Giovanna Guidoboni
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
Recent developments in the use of artificial intelligence in the diagnosis and monitoring of glaucoma are discussed. To set the context and fix terminology, a brief historic overview of artificial intelligence is provided, along with some fundamentals of statistical modeling. Next, recent applications of artificial intelligence techniques in glaucoma diagnosis and the monitoring of glaucoma progression are reviewed, including the classification of visual field images and the detection of glaucomatous change in retinal nerve fiber layer thickness. Current challenges in the direct application of artificial intelligence to further our understating of this disease are also outlined. The article also discusses how the combined use of mathematical modeling and artificial intelligence may help to address these challenges, along with stronger communication between data scientists and clinicians.
