DeepAlienorNet: A deep learning model to extract clinical features from colour fundus photography in age-related macular degeneration

Alexis Mathieu; Soufiane Ajana; Jean-François Korobelnik; Mélanie Le Goff; Brigitte Gontier; Marie-Bénédicte Rougier; Cécile Delcourt; Marie-Noëlle Delyfer

doi:10.1111/aos.16660

DeepAlienorNet: A deep learning model to extract clinical features from colour fundus photography in age-related macular degeneration

Acta Ophthalmol. 2024 Feb 12. doi: 10.1111/aos.16660. Online ahead of print.

Authors

Alexis Mathieu^{1

2}, Soufiane Ajana¹, Jean-François Korobelnik^{1

2}, Mélanie Le Goff¹, Brigitte Gontier², Marie-Bénédicte Rougier², Cécile Delcourt^{1

2}, Marie-Noëlle Delyfer^{1

2

3}

Affiliations

¹ Inserm, Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Bordeaux, France.
² Service d'Ophtalmologie, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France.
³ FRCRnet/FCRIN Network, Bordeaux, France.

PMID: 38345159
DOI: 10.1111/aos.16660

Abstract

Objective: This study aimed to develop a deep learning (DL) model, named 'DeepAlienorNet', to automatically extract clinical signs of age-related macular degeneration (AMD) from colour fundus photography (CFP).

Methods and analysis: The ALIENOR Study is a cohort of French individuals 77 years of age or older. A multi-label DL model was developed to grade the presence of 7 clinical signs: large soft drusen (>125 μm), intermediate soft (63-125 μm), large area of soft drusen (total area >500 μm), presence of central soft drusen (large or intermediate), hyperpigmentation, hypopigmentation, and advanced AMD (defined as neovascular or atrophic AMD). Prediction performances were evaluated using cross-validation and the expert human interpretation of the clinical signs as the ground truth.

Results: A total of 1178 images were included in the study. Averaging the 7 clinical signs' detection performances, DeepAlienorNet achieved an overall sensitivity, specificity, and AUROC of 0.77, 0.83, and 0.87, respectively. The model demonstrated particularly strong performance in predicting advanced AMD and large areas of soft drusen. It can also generate heatmaps, highlighting the relevant image areas for interpretation.

Conclusion: DeepAlienorNet demonstrates promising performance in automatically identifying clinical signs of AMD from CFP, offering several notable advantages. Its high interpretability reduces the black box effect, addressing ethical concerns. Additionally, the model can be easily integrated to automate well-established and validated AMD progression scores, and the user-friendly interface further enhances its usability. The main value of DeepAlienorNet lies in its ability to assist in precise severity scoring for further adapted AMD management, all while preserving interpretability.

Keywords: artificial intelligence; computer-assisted; deep learning; fundus photography; image interpretation; macular degeneration.

Abstract

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