@article {1629460,
	title = {Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis},
	journal = {Transl Vis Sci Technol},
	volume = {11},
	number = {1},
	year = {2022},
	month = {2022 01 03},
	pages = {27},
	abstract = {Purpose: Identifying and monitoring visual field (VF) defects due to optic neuritis (ON) relies on qualitative clinician interpretation. Archetypal analysis (AA), a form of unsupervised machine learning, is used to quantify VF defects in glaucoma. We hypothesized that AA can identify quantifiable, ON-specific patterns (as archetypes [ATs]) of VF loss that resemble known ON VF defects. Methods: We applied AA to a dataset of 3892 VFs prospectively collected from 456 eyes in the Optic Neuritis Treatment Trial (ONTT), and decomposed each VF into component ATs (total weight = 100\%). AA of 568 VFs from 61 control eyes was used to define a minimum meaningful (<=7\%) AT weight and weight change. We correlated baseline ON AT weights with global VF indices, visual acuity, and contrast sensitivity. For eyes with a dominant AT (weight >=50\%), we compared the ONTT VF classification with the AT pattern. Results: AA generated a set of 16 ATs containing patterns seen in the ONTT. These were distinct from control ATs. Baseline study eye VFs were decomposed into 2.9 {\textpm} 1.5 ATs. AT2, a global dysfunction pattern, had the highest mean weight at baseline (36\%; 95\% confidence interval, 33\%-40\%), and showed the strongest correlation with MD (r = -0.91; P \&lt; 0.001), visual acuity (r = 0.70; P \&lt; 0.001), and contrast sensitivity (r = -0.77; P \&lt; 0.001). Of 191 baseline VFs with a dominant AT, 81\% matched the descriptive classifications. Conclusions: AA identifies and quantifies archetypal, ON-specific patterns of VF loss. Translational Relevance: AA is a quantitative, objective method for demonstrating and monitoring change in regional VF deficits in ON.},
	keywords = {Humans, Optic Neuritis, Retrospective Studies, Vision Disorders, Visual Field Tests, Visual Fields},
	issn = {2164-2591},
	doi = {10.1167/tvst.11.1.27},
	author = {Solli, Elena and Doshi, Hiten and Tobias Elze and Pasquale, Louis and Wall, Michael and Kupersmith, Mark}
}
