Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs

• Published in: American journal of ophthalmology Vol. 211; pp. 123 - 131
• Main Authors: Jammal, Alessandro A., Thompson, Atalie C., Mariottoni, Eduardo B., Berchuck, Samuel I., Urata, Carla N., Estrela, Tais, Wakil, Susan M., Costa, Vital P., Medeiros, Felipe A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2020; Elsevier Limited
• Subjects: Aged; Algorithms; Area Under Curve; Cross-Sectional Studies; Datasets; Deep Learning; Defects; Diabetic retinopathy; Female; Fundus Oculi; Glaucoma; Glaucoma, Open-Angle - diagnosis; Glaucoma, Open-Angle - diagnostic imaging; Gonioscopy; Humans; Intraocular Pressure - physiology; Male; Middle Aged; Nerve Fibers - pathology; Neural networks; Optic nerve; Optic Nerve Diseases - diagnosis; Optic Nerve Diseases - diagnostic imaging; Optics; Photography; Physical Examination; Retinal Ganglion Cells - pathology; Retrospective Studies; ROC Curve; Software; Tomography; Tomography, Optical Coherence; Vision Disorders - diagnosis; Visual Field Tests - methods; Visual Fields - physiology

To compare the diagnostic performance of human gradings vs predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm trained to quantify retinal nerve fiber layer (RNFL) damage on fundus photographs. Evaluation of a machine learning algorithm. An M2M DL algorithm trained with R...