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The British-Israeli Project for Algorithm-Based Management of Age-Related Macular Degeneration: Deep Learning

Dinah Zur1,2, David M Wright3, Marganit Shahar Gonen1

  • 1Ophthalmology Division, Tel Aviv Medical Center, Tel Aviv, Israel.

Ophthalmologica. Journal International D'Ophtalmologie. International Journal of Ophthalmology. Zeitschrift Fur Augenheilkunde
|June 29, 2025
PubMed
Summary

This study developed an integrated dataset using deep learning to analyze optical coherence tomography (OCT) scans in neovascular age-related macular degeneration (nAMD) patients, revealing insights into fluid patterns and aiding personalized treatment.

Keywords:
Age-related macular degenerationAutomated detectionDeep learningFluid volumesReal-world data

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Neovascular age-related macular degeneration (nAMD) presents complex challenges in clinical management.
  • Integrating clinical data with optical coherence tomography (OCT) imaging is crucial for understanding disease progression.
  • Automated analysis of OCT scans can enhance the objectivity and efficiency of nAMD assessment.

Purpose of the Study:

  • To develop an integrative dataset combining clinical and OCT imaging data for nAMD.
  • To apply a deep learning (DL) algorithm for automated quantification of OCT scans in large real-world nAMD datasets.
  • To characterize baseline demographics, clinical parameters, and retinal morphological features in nAMD patients.

Main Methods:

  • Retrospective analysis of data from 5,207 eyes of 4,265 nAMD patients across two international centers.
  • Utilized a deep learning algorithm (NOA™) for automated quantification of retinal fluid volumes and morphological features from OCT scans.
  • Compared baseline characteristics, including visual acuity and fluid distribution, between UK and Israeli cohorts.

Main Results:

  • The integrated dataset comprised over 134,000 visual acuity measurements and 79,000 OCT scans.
  • Fluid distribution patterns were consistent, with most eyes exhibiting intraretinal and subretinal fluid.
  • Observed age-related trends in fluid volumes and weak correlations between baseline OCT measurements and visual acuity.

Conclusions:

  • Demonstrated the feasibility of integrating large-scale clinical and imaging data for automated nAMD analysis.
  • Provided comprehensive baseline characterization offering insights into real-world nAMD presentations.
  • Established a foundation for personalized decision-making and outcome optimization in nAMD management.