Ocular microvascular complications in diabetic retinopathy: insights from machine learning

Abstract

Introduction: Diabetic retinopathy (DR) is a leading cause of preventable blindness among working-age adults, primarily driven by ocular microvascular complications from chronic hyperglycemia. Comprehending the complex relationship between microvascular changes in the eye and disease progression poses challenges, traditional methods assuming linear or logistical relationships may not adequately capture the intricate interactions between these changes and disease advances. Hence, the aim of this study was to evaluate the microvascular involvement of diabetes mellitus (DM) and non-proliferative DR with the implementation of non-parametric machine learning methods.

Research design and methods: We conducted a retrospective cohort study that included optical coherence tomography angiography (OCTA) images collected from a healthy group (196 eyes), a DM no DR group (120 eyes), a mild DR group (71 eyes), and a moderate DR group (66 eyes). We implemented a non-parametric machine learning method for four classification tasks that used parameters extracted from the OCTA images as predictors: DM no DR versus healthy, mild DR versus DM no DR, moderate DR versus mild DR, and any DR versus no DR. SHapley Additive exPlanations values were used to determine the importance of these parameters in the classification.

Results: We found large choriocapillaris flow deficits were the most important for healthy versus DM no DR, and became less important in eyes with mild or moderate DR. The superficial microvasculature was important for the healthy versus DM no DR and mild DR versus moderate DR tasks, but not for the DM no DR versus mild DR task-the stage when deep microvasculature plays an important role. Foveal avascular zone metric was in general less affected, but its involvement increased with worsening DR.

Conclusions: The findings from this study provide valuable insights into the microvascular involvement of DM and DR, facilitating the development of early detection methods and intervention strategies.

Keywords: diabetic retinopathy.

Figure 1

Study protocol: Representative images from diabetic retinopathy using (A) color fundus photography and individual OCTA plexuses, (B) superficial capillary plexus, (C) deep capillary plexus, and (D) choriocapillaris plexus. Microvascular features were extracted from the OCTA images and used as predictors for a machine learning classifier to discriminate between healthy, DM no DR, mild DR, and moderate DR eyes. The feature importance of the microvascular parameters was calculated from the classifiers built. DM, diabetes mellitus; DR, diabetic retinopathy; OCTA, optical coherence tomography angiography.

Figure 2

Feature importance results: (A) SHAP value plots for the four classification tasks: DM no DR versus healthy, mild DR versus DM no DR, moderate DR versus mild DR, and any DR versus no DR. Each of the points on the SHAP plots is representative of the calculated SHAP value of the parameter for a single testing sample, the color denotes the value of the parameter. Negative SHAP values show that the parameter is pushing the predicted probability towards a class 0 prediction and vice versa. The y-axis arrangement of the parameters is arranged based on the absolute sum of the SHAP values. The change in the ranking of the parameters demonstrates that different microvascular parameters are important for the classification tasks. (B) Ranking of the parameters based on SHAP values: The large and small flow deficit parameters were highly ranked when detecting the presence of DM and mild DR, the deep vessel density was the most important in distinguishing DM from mild DR, the superficial vessel density was consistently highly ranked except in the mild DR versus DM no DR classification and the FAZ perimeter was consistently ranked low except in the moderate DR versus mild DR classification. DM, diabetes mellitus; DR, diabetic retinopathy; FAZ, foveal avascular zone; SHAP, SHapley Additive exPlanations.