TY - JOUR
T1 - Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis
AU - Solli, Elena
AU - Doshi, Hiten
AU - Elze, Tobias
AU - Pasquale, Louis
AU - Wall, Michael
AU - Kupersmith, Mark
N1 - Publisher Copyright:
© 2022 The Authors.
PY - 2022/1
Y1 - 2022/1
N2 - 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 ± 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 < 0.001), visual acuity (r = 0.70; P < 0.001), and contrast sensitivity (r =−0.77; P < 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.
AB - 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 ± 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 < 0.001), visual acuity (r = 0.70; P < 0.001), and contrast sensitivity (r =−0.77; P < 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.
KW - archetypal analysis
KW - deep learning
KW - optic neuritis
KW - visual field
UR - http://www.scopus.com/inward/record.url?scp=85123651451&partnerID=8YFLogxK
U2 - 10.1167/tvst.11.1.27
DO - 10.1167/tvst.11.1.27
M3 - Article
C2 - 35044445
AN - SCOPUS:85123651451
SN - 2164-2591
VL - 11
JO - Translational Vision Science and Technology
JF - Translational Vision Science and Technology
IS - 1
M1 - 27
ER -