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Machine learning OCT predictors of progression from intermediate age-related macular degeneration to geographic atrophy and vision loss.

Publication ,  Journal Article
Lad, E; Sleiman, K; Banks, DL; Hariharan, S; Clemons, T; Herrmann, R; Dauletbekov, D; Giani, A; Chong, V; Chew, EY; Toth, CA
Published in: Ophthalmol Sci
June 2022

OBJECTIVE: To describe optical coherence tomography (SD-OCT) features, age, gender, and systemic variables that may be used in machine/deep learning studies to identify high-risk patient subpopulations with high risk of progression to geographic atrophy (GA) and visual acuity (VA) loss in the short term. DESIGN: prospective, longitudinal study. SUBJECTS: We analyzed imaging data from patients with iAMD (N= 316) enrolled in Age-Related Eye Disease Study 2 (AREDS2) Ancillary SD-OCT with adequate SD-OCT imaging for repeated measures. METHODS: Qualitative and quantitative multimodal variables from the database were derived at each yearly visit over 5 years. Based on statistical analyses developed in the field of cardiology, an algorithm was developed and used to select person-years without GA on colour fundus photography or SD-OCT at baseline. The analysis employed machine learning approaches to generate classification trees. Eyes were stratified as low, average, above average and high risk in 1 or 2 years, based on OCT and demographic features by the risk of GA development or decreased VA by 5+ and 10+ letters. MAIN OUTCOME MEASURES: new onset of SD-OCT-determined GA and VA loss. RESULTS: We identified multiple retinal and subretinal SD-OCT and demographic features from the baseline visit, each of which independently conveyed low to high risk of new-onset GA or VA loss on each of the follow-up visits at 1 or 2 years. CONCLUSION: We propose a risk-stratified classification of iAMD based on the combination of OCT-derived retinal features, age, gender and systemic variables for progression to OCT-determined GA and/or VA loss. After external validation, the composite early endpoints may be used as exclusion or inclusion criteria for future clinical studies of iAMD focused on prevention of GA progression or VA loss.

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Published In

Ophthalmol Sci

DOI

EISSN

2666-9145

Publication Date

June 2022

Volume

2

Issue

2

Location

Netherlands
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Lad, E., Sleiman, K., Banks, D. L., Hariharan, S., Clemons, T., Herrmann, R., … Toth, C. A. (2022). Machine learning OCT predictors of progression from intermediate age-related macular degeneration to geographic atrophy and vision loss. Ophthalmol Sci, 2(2). https://doi.org/10.1016/j.xops.2022.100160
Lad, Eleonora, Karim Sleiman, David L. Banks, Sanjay Hariharan, Traci Clemons, Rolf Herrmann, Daniyar Dauletbekov, et al. “Machine learning OCT predictors of progression from intermediate age-related macular degeneration to geographic atrophy and vision loss.Ophthalmol Sci 2, no. 2 (June 2022). https://doi.org/10.1016/j.xops.2022.100160.
Lad E, Sleiman K, Banks DL, Hariharan S, Clemons T, Herrmann R, et al. Machine learning OCT predictors of progression from intermediate age-related macular degeneration to geographic atrophy and vision loss. Ophthalmol Sci. 2022 Jun;2(2).
Lad, Eleonora, et al. “Machine learning OCT predictors of progression from intermediate age-related macular degeneration to geographic atrophy and vision loss.Ophthalmol Sci, vol. 2, no. 2, June 2022. Pubmed, doi:10.1016/j.xops.2022.100160.
Lad E, Sleiman K, Banks DL, Hariharan S, Clemons T, Herrmann R, Dauletbekov D, Giani A, Chong V, Chew EY, Toth CA. Machine learning OCT predictors of progression from intermediate age-related macular degeneration to geographic atrophy and vision loss. Ophthalmol Sci. 2022 Jun;2(2).

Published In

Ophthalmol Sci

DOI

EISSN

2666-9145

Publication Date

June 2022

Volume

2

Issue

2

Location

Netherlands