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Artificial intelligence (AI) can now predict visual acuity and retinal sensitivity in age-related macular degeneration (AMD) using multimodal imaging. These AI-driven insights offer reliable surrogate endpoints for clinical trials, advancing AMD research.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Sensitive outcome measures are crucial for age-related macular degeneration (AMD) clinical trials.
  • Artificial intelligence (AI) shows promise in inferring psychophysical results from multimodal imaging.

Purpose of the Study:

  • To review the current literature on AI applications for inferring retinal function in AMD.
  • To assess the accuracy and potential of AI-based structure-function correlations as surrogate endpoints.

Main Methods:

  • Literature review using PubMed and Web of Science.
  • Keywords: 'artificial intelligence', 'machine learning', 'perimetry', 'best-corrected visual acuity (BCVA)', 'retinal function', 'age-related macular degeneration'.

Main Results:

  • AI can infer best-corrected visual acuity (BCVA) with accuracy comparable to actual assessment (RMSE ~7-11 letters).
  • AI accurately infers fundus-controlled perimetry (FCP) results (mesopic and dark-adapted), with accuracy improving with added short FCP (MAE ~3-5 dB).

Conclusions:

  • Inferred BCVA and inferred retinal sensitivity using AI and multimodal imaging are viable quasi-functional surrogate endpoints.
  • These AI-driven measures can potentially streamline future interventional clinical trials for AMD.