Method aids glaucoma discrimination

New technique improves diffrentiation between normal and glaucomatous eyes

The new technique, developed by the Glaucoma Imaging Group at the University of Pittsburgh Medical Center (UPMC) Eye Center under the direction of Dr Joel Schuman, chairman, is based on superpixel machineclassifier analysis of three-dimensional (3D) spectral-domain optical coherence tomography (SDOCT). While the faster scanning speed of SDOCT allows 3D volume scanning of the retinal layers and enhances early detection of glaucoma and monitoring of disease, these structural measurements do not take full advantage of the 3D dataset, Dr Xu noted.

Only 512 sampling points out of 40000 are used in the conventional retinal nerve fiber layer (RNFL) analysis, and signs of pathologic damage may be missed.