Open AccessProtocol for the diagnosis of keratoconus using convolutional neural networks
Author(s) -
Jan Schatteburg,
Achim Langenbucher
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0264219
Subject(s) - convolutional neural network , keratoconus , protocol (science) , computer science , computational biology , bioinformatics , artificial intelligence , medicine , biology , ophthalmology , pathology , alternative medicine , cornea
Keratoconus is the corneal disease with the highest reported incidence of 1:2000. The treatment’s level of success highly depends on how early it was started. Subsequently, a fast and highly capable diagnostic tool is crucial. While there are many computer-based systems that are capable of the analysis of medical image data, they only provide parameters. These have advanced quite far, though full diagnosis does not exist. Machine learning has provided the capabilities for the parameters, and numerous similar scientific fields have developed full image diagnosis based on neural networks. The Homburg Keratoconus Center has been gathering almost 2000 patient datasets, over 1000 of them over the course of their disease. Backed by this databank, this work aims to develop a convolutional neural network to tackle diagnosis of keratoconus as the major corneal disease.