Radial polarisation patterns identify macular damage: a machine learning approach

Radially structured polarisation-modulated geometric patterns outperform polarisation-modulated optotypes and standard logMAR acuity measures in predicting macular damage, regardless of foveolar involvement.

Authors

Institutions

Publication Details

Journal
Clinical and Experimental Optometry
Published
2024-10-07
DOI
https://doi.org/10.1080/08164622.2024.2410890
Citations
1
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.40

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Radial polarisation patterns identify macular damage: a machine learning approach

Gary P. Misson, Mark Dunne, Stephen J. Anderson
1 citations
Clinical and Experimental Optometry
Retinal Imaging and Analysis
0.40
article

Radial polarisation patterns identify macular damage: a machine learning approach

Gary P. Misson, Mark Dunne, Stephen J. Anderson
article en
1 citations

Abstract

Radially structured polarisation-modulated geometric patterns outperform polarisation-modulated optotypes and standard logMAR acuity measures in predicting macular damage, regardless of foveolar involvement.

Clinical and Experimental OptometryVol. 108(6)
Aston University (GB)
European Society of Cataract and Refractive Surgeons
Openalex Percentile: Top 34%
Retinal Imaging and Analysis
0.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.