Open AccessUtilizing human intelligence in artificial intelligence for detecting glaucomatous fundus images using human-in-the-loop machine learning
Author(s) -
Ramesh Rajasekaran,
Tamilselvan Subramaniam,
Prajnya Ray,
Aji Kunnath Devadas,
Shruthy Vaishali Ramesh,
Sheik Mohamed Ansar,
Meena Kumari Ramesh,
Ramesh Rajasekaran,
Sathyan Parthasarathi
Publication year - 2022
Publication title -
indian journal of ophthalmology/indian journal of ophthalmology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.542
H-Index - 51
eISSN - 1998-3689
pISSN - 0301-4738
DOI - 10.4103/ijo.ijo_2583_21
Subject(s) - glaucoma , fundus (uterus) , artificial intelligence , convolutional neural network , medicine , computer science , ophthalmology , optic nerve , deep learning , computer vision
For diagnosing glaucomatous damage, we have employed a novel convolutional neural network (CNN) from TrueColor confocal fundus images to conquer the black box dilemma in artificial intelligence (AI). This neural network with CNN architecture with human-in-the-loop (HITL) data annotation helps not only in diagnosing glaucoma but also in predicting and locating detailed signs in the glaucomatous fundus, such as splinter hemorrhages, glaucomatous optic atrophy, vertical glaucomatous cupping, peripapillary atrophy, and retinal nerve fiber layer (RNFL) defect.