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dc.contributor.authorWang, M.
dc.contributor.authorShen, L.Q.
dc.contributor.authorPasquale, L.R.
dc.date.accessioned2019-03-29T14:47:37Z
dc.date.available2019-03-29T14:47:37Z
dc.date.issued2019
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85060529303&doi=10.1167%2fiovs.18-25568&partnerID=40&md5=3c77dd1d1e326ac156968b4b9494378f
dc.identifier.urihttp://hdl.handle.net/10713/8699
dc.description.abstractPurpose: 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.en_US
dc.description.urihttps://dx.doi.org/10.1167/iovs.18-25568en_US
dc.language.isoen_USen_US
dc.publisherAssociation for Research in Vision and Ophthalmologyen_US
dc.relation.ispartofInvestigative ophthalmology & visual science
dc.subjectspatial pattern analysisen_US
dc.subject.meshGlaucomaen_US
dc.subject.meshVisual Field Tests--methodsen_US
dc.subject.meshVisual Field Tests--statistics & numerical dataen_US
dc.subject.meshArtificial Intelligenceen_US
dc.titleAn Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1167/iovs.18-25568
dc.identifier.pmid30682206


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