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Related Experiment Video

Updated: Sep 13, 2025

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
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Retinograd-AI: An Open-Source Automated Fundus Autofluorescence Retinal Image Gradability Assessment for Inherited

Gunjan Naik1,2,3, Saoud Al-Khuzaei4,5, Ismail Moghul1,2,3

  • 1Faculty of Brain Sciences, UCL Institute of Ophthalmology, London, United Kingdom.

Ophthalmology Science
|July 31, 2025
PubMed
Summary
This summary is machine-generated.

An AI tool, Retinograd-AI, accurately assesses the quality of fundus autofluorescence (FAF) images in inherited retinal diseases (IRDs). This automated system improves large-scale retinal image analysis and aids in developing robust diagnostic pipelines for IRDs.

Keywords:
AIFundus AutofluorescenceGradabilityImage qualityInherited retinal diseases

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Inherited retinal diseases (IRDs) require high-quality fundus autofluorescence (FAF) imaging for accurate diagnosis and monitoring.
  • Assessing FAF image quality is crucial for reliable analysis and the development of AI-driven diagnostic tools.

Purpose of the Study:

  • To develop and validate an automated artificial intelligence (AI) system, Retinograd-AI, for assessing the gradability of FAF images in patients with IRDs.

Main Methods:

  • A dataset of 2445 FAF images from IRD patients was graded by expert graders.
  • An AI algorithm, Retinograd-AI, was trained on this dataset to predict image gradability.
  • The AI model was applied to a larger dataset of 136,631 FAF images and validated against human assessments.

Main Results:

  • Retinograd-AI achieved 91% accuracy and an AUC of 0.94 in distinguishing gradable from ungradable FAF images.
  • The AI model improved the accuracy of a gene prediction classifier from 33.8% to 68.9%.
  • Analysis revealed associations between image gradability, patient demographics (sex), and specific genetic diagnoses.

Conclusions:

  • Retinograd-AI provides an accurate, open-source solution for automated FAF image quality assessment in IRDs.
  • This tool facilitates large-scale retinal image analysis and supports the clinical deployment of AI algorithms.
  • Future extensions of Retinograd-AI will encompass other retinal conditions through transfer learning.