Study Results from Guilan University of Medical Sciences Provide New Insights into Glaucoma (Vessel Density Features of Optical Coherence Tomography Angiography for Classification of Glaucoma Using Machine Learning).

A study conducted by researchers at Guilan University of Medical Sciences in Iran has found that machine learning algorithms can effectively detect glaucoma using optical coherence tomography angiography (OCT-A) images. The study analyzed images from 119 glaucoma patients and 76 healthy individuals, and developed four vessel density features using a threshold-based segmentation method. The support vector machine (SVM) classifier achieved the best results, accurately discriminating glaucoma from healthy eyes with an area under the receiver-operating-characteristic curve (AUC) of 1 and accuracy of 1. The study concludes that machine learning based on vessel density features of OCT-A images can provide excellent performance for glaucoma detection. [Extracted from the article]