Assessment of Parafoveal Diabetic Macular Ischemia on Optical Coherence Tomography Angiography Images to Predict Diabetic Retinal Disease Progression and Visual Acuity Deterioration

Abstract

ImportanceThe presence of diabetic macular ischemia (DMI) on optical coherence tomography angiography (OCTA) images predicts diabetic retinal disease progression and visual acuity (VA) deterioration, suggesting an OCTA-based DMI evaluation can further enhance diabetic retinopathy (DR) management.ObjectiveTo investigate whether an automated binary DMI algorithm using OCTA images provides prognostic value on DR progression, diabetic macular edema (DME) development, and VA deterioration in a cohort of patients with diabetes.Design, Setting, and ParticipantsIn this cohort study, DMI assessment of superficial capillary plexus and deep capillary plexus OCTA images was performed by a previously developed deep learning algorithm. The presence of DMI was defined as images exhibiting disruption of fovea avascular zone with or without additional areas of capillary loss, while absence of DMI was defined as images presented with intact fovea avascular zone outline and normal distribution of vasculature. Patients with diabetes were recruited starting in July 2015 and were followed up for at least 4 years. Cox proportional hazards models were used to evaluate the association of the presence of DMI with DR progression, DME development, and VA deterioration. Analysis took place between June and December 2022.Main Outcomes and MeasuresDR progression, DME development, and VA deterioration.ResultsA total of 321 eyes from 178 patients were included for analysis (85 [47.75%] female; mean [SD] age, 63.39 [11.04] years). Over a median (IQR) follow-up of 50.41 (48.16-56.48) months, 105 eyes (32.71%) had DR progression, 33 eyes (10.28%) developed DME, and 68 eyes (21.18%) had VA deterioration. Presence of superficial capillary plexus–DMI (hazard ratio [HR], 2.69; 95% CI, 1.64-4.43; P < .001) and deep capillary plexus–DMI (HR, 3.21; 95% CI, 1.94-5.30; P < .001) at baseline were significantly associated with DR progression, whereas presence of deep capillary plexus–DMI was also associated with DME development (HR, 4.60; 95% CI, 1.15-8.20; P = .003) and VA deterioration (HR, 2.12; 95% CI, 1.01-5.22; P = .04) after adjusting for age, duration of diabetes, fasting glucose, glycated hemoglobin, mean arterial blood pressure, DR severity, ganglion cell–inner plexiform layer thickness, axial length, and smoking at baseline.Conclusions and RelevanceIn this study, the presence of DMI on OCTA images demonstrates prognostic value for DR progression, DME development, and VA deterioration.

A deep learning system for predicting time to progression of diabetic retinopathy

“…In our study, retinal vascular geometry and the fovea were important image patterns for future occurrence of DR based on the DeepDR Plus system, which was consistent with state-of-the-art studies and our previous studies 29 – 31 , 37 , 38 . Regions of DR-related capillary dropout have been related to local underlying photoreceptor loss, and the fovea may provide information on visual function and foveal perfusion 39 , 40 .…”

“…It has been demonstrated that vessel density, fractal dimension and foveal avascular zone area could predict DR progression 29 , 30 . Previous studies also support that retinal vascular changes and related variables are associated with DR-related risk factors 31 .…”

Global research trends and future directions in diabetic macular edema research: A bibliometric and visualized analysis

“…[35,[86][87][88][89] Many researchers have integrated diverse data sources, including fundus images, clinical data, genomic information, and extensive long-term monitoring data, to build predictive models for forecasting the occurrence, progression, and treatment response of DME. [90][91][92][93][94][95] Personalized treatment plans based on deep learning have also attracted considerable attention. Analyzing large-scale clinical data and establishing models to provide customized, individualized treatment plans for each patient can improve treatment outcomes.…”