A deep learning approach to explore the association of age-related macular degeneration polygenic risk score with retinal optical coherence tomography: A preliminary study.

Autor: Sendecki A; Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland., Ledwoń D; Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland., Nycz J; Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany., Wąsowska A; Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland.; Genomed S.A., Warszawa, Poland., Boguszewska-Chachulska A; Genomed S.A., Warszawa, Poland., Mitas AW; Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland., Wylęgała E; Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland., Teper S; Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland.
Jazyk: angličtina
Zdroj: Acta ophthalmologica [Acta Ophthalmol] 2024 Nov; Vol. 102 (7), pp. e1029-e1039. Date of Electronic Publication: 2024 May 18.
DOI: 10.1111/aos.16710
Abstrakt: Purpose: Age-related macular degeneration (AMD) is a complex eye disorder affecting millions worldwide. This article uses deep learning techniques to investigate the relationship between AMD, genetics and optical coherence tomography (OCT) scans.
Methods: The cohort consisted of 332 patients, of which 235 were diagnosed with AMD and 97 were controls with no signs of AMD. The genome-wide association studies summary statistics utilized to establish the polygenic risk score (PRS) in relation to AMD were derived from the GERA European study. A PRS estimation based on OCT volumes for both eyes was performed using a proprietary convolutional neural network (CNN) model supported by machine learning models. The method's performance was assessed using numerical evaluation metrics, and the Grad-CAM technique was used to evaluate the results by visualizing the features learned by the model.
Results: The best results were obtained with the CNN and the Extra Tree regressor (MAE = 0.55, MSE = 0.49, RMSE = 0.70, R 2  = 0.34). Extending the feature vector with additional information on AMD diagnosis, age and smoking history improved the results slightly, with mainly AMD diagnosis used by the model (MAE = 0.54, MSE = 0.44, RMSE = 0.66, R 2  = 0.42). Grad-CAM heatmap evaluation showed that the model decisions rely on retinal morphology factors relevant to AMD diagnosis.
Conclusion: The developed method allows an efficient PRS estimation from OCT images. A new technique for analysing the association of OCT images with PRS of AMD, using a deep learning approach, may provide an opportunity to discover new associations between genotype-based AMD risk and retinal morphology.
(© 2024 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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