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Determinants of Cone and Rod Functions in Geographic Atrophy: AI-Based Structure-Function Correlation
Maximilian Pfau1, Leon von der Emde2, Chantal Dysli3
1Department of Ophthalmology, University of Bonn, Bonn, Germany; GRADE Reading Center, Bonn, Germany; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
American Journal of Ophthalmology
|April 15, 2020
Summary
Artificial intelligence (AI) accurately predicts retinal function in geographic atrophy (GA) by analyzing retinal microstructure. This AI-driven approach can monitor disease progression and serve as a surrogate outcome in clinical trials for age-related macular degeneration (AMD).
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Geographic atrophy (GA) is a severe form of age-related macular degeneration (AMD) characterized by progressive vision loss.
- Understanding the relationship between retinal microstructure and visual function in GA is crucial for disease management and therapeutic development.
- Current methods for assessing visual function may not fully capture the extent of functional loss in GA, especially in regions outside the atrophic areas.
Purpose of the Study:
- To investigate the association between retinal microstructure and cone and rod function in GA secondary to AMD using artificial intelligence (AI) algorithms.
- To develop and validate AI models for predicting retinal sensitivity based on structural imaging data.
- To explore the utility of AI-driven functional mapping in monitoring GA progression.
Main Methods:
- Prospective, observational case series including 41 eyes of 41 patients with GA.
- Assessment of mesopic and dark-adapted (DA) retinal sensitivities using fundus-controlled perimetry (microperimetry).
- Evaluation of retinal microstructure using spectral-domain optical coherence tomography (SD-OCT), fundus autofluorescence (FAF), and near-infrared reflectance (IR) imaging.
- Application of random forest algorithms for predicting retinal sensitivity from structural data, assessing feature importance (%IncMSE).
Main Results:
- AI models accurately predicted retinal sensitivity (mean absolute error [MAE] ranging from 4.40 to 4.89 dB) without patient-specific data.
- Incorporating patient-specific data significantly improved prediction accuracy (MAE reduced to 2.77–2.89 dB).
- Outer nuclear layer thickness was the most important predictive feature for retinal sensitivity across all testing conditions.
- Spatially resolved mapping revealed functional deficits in areas outside of visible GA lesions.
Conclusions:
- AI-driven "inferred sensitivity" mapping accurately reflects retinal function in patients with GA.
- "Inferred sensitivity" mapping can facilitate monitoring of GA disease progression.
- This AI approach offers a "quasi-functional" surrogate outcome measure for clinical trials, particularly for assessing regions beyond GA lesions.
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