Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

• Published in: Investigative ophthalmology & visual science Vol. 46; no. 11; pp. 4147 - 4152
• Main Authors: Burgansky-Eliash, Zvia, Wollstein, Gadi, Chu, Tianjiao, Ramsey, Joseph D, Glymour, Clark, Noecker, Robert J, Ishikawa, Hiroshi, Schuman, Joel S
• Format: Journal Article
• Language: English
• Published: Rockville, MD ARVO 01.11.2005; Association for Research in Vision and Ophtalmology
• Subjects: Adult; Aged; Aged, 80 and over; Biological and medical sciences; Cross-Sectional Studies; Diagnostic Techniques, Ophthalmological - classification; Eye and associated structures. Visual pathways and centers. Vision; Female; Fundamental and applied biological sciences. Psychology; Glaucoma and intraocular pressure; Glaucoma, Open-Angle - classification; Glaucoma, Open-Angle - diagnosis; Humans; Intraocular Pressure; Male; Medical sciences; Middle Aged; Nerve Fibers - parasitology; Neural Networks, Computer; Ophthalmology; Optic Nerve Diseases - diagnosis; Pilot Projects; Reproducibility of Results; Retinal Ganglion Cells - parasitology; ROC Curve; Sensitivity and Specificity; Tomography, Optical Coherence - classification; Vertebrates: nervous system and sense organs; Learning; Glaucoma; Protozoa; Eye disease; Optical coherence tomography; Glaucoma (eye); Ciliata; Ophthalmology; Medical screening; Detection

Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection....