Automated machine learning may fast detect visual field loss patterns in glaucoma

In a new study conducted by Siamak Yousefi and colleagues, it was found that an automated machine learning method can detect patterns of visual field (VF) loss and provide objective, reproducible terminology for describing early indicators of visual abnormalities and rapid progression in glaucoma patients. The findings of this study were published in Ophthalmology. This was a cross-sectional and longitudinal study that followed 2231 aberrant VFs from 205 eyes of 176 OHTS individuals for almost 16 years. An unsupervised deep archetypal analysis method and an OHTS certified VF reader were used to discover common patterns of VF loss. Machine-identified glaucoma damage patterns were compared to those previously described (expert-identified) in the OHTS in 2003.