Open AccessChoriocapillaris evaluation in choroideremia using optical coherence tomography angiography
Author(s) -
Simon S. Gao,
Rajan S. Patel,
Nieraj Jain,
Miao Zhang,
Richard G. Weleber,
David Huang,
Mark E. Pennesi,
Yali Jia
Publication year - 2016
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.8.000048
Subject(s) - choroideremia , artificial intelligence , optical coherence tomography angiography , computer science , optical coherence tomography , grading (engineering) , classifier (uml) , pattern recognition (psychology) , random forest , computer vision , medicine , ophthalmology , retinal , civil engineering , engineering
The choriocapillaris plays an important role in supporting the metabolic demands of the retina. Studies of the choriocapillaris in disease states with optical coherence tomography angiography (OCTA) have proven insightful. However, image artifacts complicate the identification and quantification of the choriocapillaris in degenerative diseases such as choroideremia. Here, we demonstrate a supervised machine learning approach to detect intact choriocapillaris based on training with results from an expert grader. We trained a random forest classifier to evaluate en face structural OCT and OCTA information along with spatial image features. Evaluation of the trained classifier using previously unseen data showed good agreement with manual grading.