TY - JOUR
T1 - An artificial intelligence approach to detect visual field progression in glaucoma based on spatial pattern analysis
AU - Wang, Mengyu
AU - Shen, Lucy Q.
AU - Pasquale, Louis R.
AU - Petrakos, Paul
AU - Formica, Sydney
AU - Boland, Michael V.
AU - Wellik, Sarah R.
AU - De Moraes, Carlos Gustavo
AU - Myers, Jonathan S.
AU - Saeedi, Osamah
AU - Wang, Hui
AU - Baniasadi, Neda
AU - Li, Dian
AU - Tichelaar, Jorryt
AU - Bex, Peter J.
AU - Elze, Tobias
N1 - Publisher Copyright:
© 2019 The Authors.
PY - 2019/1
Y1 - 2019/1
N2 - PURPOSE. To detect visual field (VF) progression by analyzing spatial pattern changes. METHODS. We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists. RESULTS. In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P < 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60). CONCLUSIONS. The archetype method can inform clinicians of VF progression patterns.
AB - PURPOSE. To detect visual field (VF) progression by analyzing spatial pattern changes. METHODS. We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists. RESULTS. In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P < 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60). CONCLUSIONS. The archetype method can inform clinicians of VF progression patterns.
KW - Unsupervised artificial intelligence
KW - Visual field patterns
KW - Visual field progression
UR - http://www.scopus.com/inward/record.url?scp=85060529303&partnerID=8YFLogxK
U2 - 10.1167/iovs.18-25568
DO - 10.1167/iovs.18-25568
M3 - Article
C2 - 30682206
AN - SCOPUS:85060529303
SN - 0146-0404
VL - 60
SP - 365
EP - 375
JO - Investigative Ophthalmology and Visual Science
JF - Investigative Ophthalmology and Visual Science
IS - 1
ER -