SD-OCT Biomarkers and the Current Status of Artificial Intelligence in Predicting Progression from Intermediate to Advanced AMD

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

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, R2 = 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, R2 = 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.

Validation of an Automated Artificial Intelligence Algorithm for the Quantification of Major OCT Parameters in Diabetic Macular Edema

Artificial intelligence (AI) and deep learning (DL)-based systems have gained wide interest in macular disorders, including diabetic macular edema (DME). This paper aims to validate an AI algorithm for identifying and quantifying different major optical coherence tomography (OCT) biomarkers in DME eyes by comparing the algorithm to human expert manual examination. Intraretinal (IRF) and subretinal fluid (SRF) detection and volumes, external limiting-membrane (ELM) and ellipsoid zone (EZ) integrity, and hyperreflective retina foci (HRF) quantification were analyzed. Three-hundred three DME eyes were included. The mean central subfield thickness was 386.5 ± 130.2 µm. IRF was present in all eyes and confirmed by AI software. The agreement (kappa value) (95% confidence interval) for SRF presence and ELM and EZ interruption were 0.831 (0.738–0.924), 0.934 (0.886–0.982), and 0.936 (0.894–0.977), respectively. The accuracy of the automatic quantification of IRF, SRF, ELM, and EZ ranged between 94.7% and 95.7%, while accuracy of quality parameters ranged between 99.0% (OCT layer segmentation) and 100.0% (fovea centering). The Intraclass Correlation Coefficient between clinical and automated HRF count was excellent (0.97). This AI algorithm provides a reliable and reproducible assessment of the most relevant OCT biomarkers in DME. It may allow clinicians to routinely identify and quantify these parameters, offering an objective way of diagnosing and following DME eyes.

Challenges in Age-Related Macular Degeneration: From Risk Factors to Novel Diagnostics and Prevention Strategies

Age-related macular degeneration (AMD) is a chronic multifactorial eye disease representing the primary cause of vision loss in people aged 60 years and older. The etiopathogenesis of the disease remains uncertain, with several risk factors contributing to its onset and progression, such as genotype, aging, hypertension, smoking, overweight, and low dietary intake of carotenoids. Since the aging populations of the industrialized world are increasing rapidly, the impact of AMD in the socio-economical life-developed countries is expected to increase dramatically in the next years. In this context, the benefits of prevention and early disease detection for prompt and effective treatment can be enormous to reduce the social and economic burden of AMD. Nutritional and lifestyle changes, including dietary intake of xanthophyll pigments, such as lutein and zeaxanthin, no smoking, and regular exercise, are known to protect from risk of AMD progression from early to advanced disease stages. In this review, we present the clinical outcomes of a pilot study on trans-scleral iontophoresis delivery of lutein in patients with AMD. Topical delivery of lutein directly to the macula may provide a more efficient method for enriching the macular pigment and for achieving greater patient compliance to therapy than oral administration and thus enhancing prevention strategies. Modern diagnostic methodologies shall address the major problem of accurately detecting the risk of transition from intermediate AMD to advanced AMD stages. Adaptive optics retinal imaging and resonance Raman spectroscopy are two highly promising technologies for the objective assessment of patients with AMD. In this review, we present some of their clinical applications for collecting quantitative measurements of retinal cellular changes and macular content of xanthophyll pigments, respectively. In conclusion, there is great expectation that technological advancements in AMD management will deliver improved screening, therapeutic prevention, and diagnostic systems in the coming decade through a pro-active strategy of “treatment for prevention” that will aim to reduce the global burden of vision loss caused by AMD in the elderly.