Patterns of retinal nerve fiber layer loss in patients with glaucoma identified by deep archetypal analysis

An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging

“…Several studies, including ours, have proposed unsupervised and supervised machine learning approaches for detecting glaucoma progression based on OCT data or identifying patterns of RNFL loss in patients with glaucoma. 28 , 41 , 42 In this study, however, we developed unsupervised and supervised machine learning models to stage glaucoma based on OCT. Although glaucoma progression and staging are both important clinical measurements, establishing glaucoma damage stage may be particularly critical and valuable in determining treatment options and even detecting progression.…”

“…Averaging segmented A-scans (provided by the Spectralis Software) reduces the effect of variation due to possible imaging misalignment or previous anatomic differences in different eyes. 28 The input to the unsupervised model included 64 RNFL sectors along with 7 instrument-generated general sectoral and global RNFL thickness measurements.…”

“…Machine learning models of artificial intelligence systems have shown great promise for addressing different challenges in glaucoma, although most of these models have been applied to glaucoma diagnosis or progression detection. 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 In this paper, we propose unsupervised and supervised machine learning models to stage structural RNFL loss. Our system may be used to gain some insights to glaucoma when it is interpreted along with other potential ocular comorbidities that may impact RNFL.…”