Measurements of OCT Angiography Complement OCT for Diagnosing Early Primary Open-Angle Glaucoma

PurposeTo compare measurements of global and regional circumpapillary capillary density (cpCD) with retinal nerve fiber layer (RNFL) thickness and characterize their relationship with visual function in early primary open-angle glaucoma (POAG).DesignCross-sectional study.ParticipantsEighty healthy eyes, 64 preperimetric eyes, and 184 mild POAG eyes from the Diagnostic Innovations in Glaucoma Study.MethodsGlobal and regional RNFL thickness and cpCD measurements were obtained using OCT and OCT angiography (OCTA). For direct comparison at the individual and diagnostic group level, RNFL thickness and capillary density values were converted to a normalized relative loss scale.Main outcome measuresRetinal nerve fiber layer thickness and cpCD normalized loss at the individual level and diagnostic group. Global and regional areas under the receiver operating characteristic curve (AUROC) for RNFL thickness and cpCD to detect preperimetric glaucoma and glaucoma, R2 for the strength of associations between RNFL thickness function and capillary density function in diagnostic groups.ResultsBoth global and regional RNFL thickness and cpCD decreased progressively with increasing glaucoma severity (P < 0.05, except for temporal RNFL thickness). Global and regional cpCD relative loss values were higher than those of RNFL thickness (P < 0.05) in preperimetric glaucoma (except for the superonasal region) and glaucoma (except for the inferonasal and superonasal regions) eyes. Race, intraocular pressure (IOP), and cpCD were associated with greater cpCD than RNFL thickness loss in early glaucoma at the individual level (P < 0.05). Global measurements of capillary density (whole image capillary density and cpCD) had higher diagnostic accuracies than RNFL thickness in detecting preperimetric glaucoma and glaucoma (P < 0.05; except for cpCD/RNFL thickness comparison in glaucoma [P = 0.059]). Visual function was significantly associated with RNFL thickness and cpCD globally and in all regions (P < 0.05, except for temporal RNFL thickness-function association [P = 0.070]).ConclusionsAssociations between capillary density and visual function were found in the regions known to be at highest risk for damage in preperimetric glaucoma eyes and all regions of mild glaucoma eyes. In early glaucoma, capillary density loss was more pronounced than RNFL thickness loss. Individual characteristics influence the relative magnitudes of capillary density loss compared with RNFL thickness loss. Retinal nerve fiber layer thickness and microvascular assessments are complementary and yield valuable information for the detection of early damages seen in POAG

Automated machine learning for predicting diabetic retinopathy progression from ultra-widefield retinal images

Importance Machine learning (ML) algorithms have the potential to identify eyes with early diabetic retinopathy (DR) at increased risk for disease progression. Objective To create and validate automated ML models (autoML) for DR progression from ultra-widefield (UWF) retinal images.Design, Setting and Participants Deidentified UWF images with mild or moderate nonproliferative DR (NPDR) with 3 years of longitudinal follow-up retinal imaging or evidence of progression within 3 years were used to develop automated ML models for predicting DR progression in UWF images. All images were collected from a tertiary diabetes-specific medical center retinal image dataset. Data were collected from July to September 2022.Exposure Automated ML models were generated from baseline on-axis 200° UWF retinal images. Baseline retinal images were labeled for progression based on centralized reading center evaluation of baseline and follow-up images according to the clinical Early Treatment Diabetic Retinopathy Study severity scale. Images for model development were split 8-1-1 for training, optimization, and testing to detect 1 or more steps of DR progression. Validation was performed using a 328-image set from the same patient population not used in model development.Main Outcomes and Measures Area under the precision-recall curve (AUPRC), sensitivity, specificity, and accuracy.Results A total of 1179 deidentified UWF images with mild (380 [32.2%]) or moderate (799 [67.8%]) NPDR were included. DR progression was present in half of the training set (590 of 1179 [50.0%]). The model’s AUPRC was 0.717 for baseline mild NPDR and 0.863 for moderate NPDR. On the validation set for eyes with mild NPDR, sensitivity was 0.72 (95% CI, 0.57-0.83), specificity was 0.63 (95% CI, 0.57-0.69), prevalence was 0.15 (95% CI, 0.12-0.20), and accuracy was 64.3%; for eyes with moderate NPDR, sensitivity was 0.80 (95% CI, 0.70-0.87), specificity was 0.72 (95% CI, 0.66-0.76), prevalence was 0.22 (95% CI, 0.19-0.27), and accuracy was 73.8%. In the validation set, 6 of 8 eyes (75%) with mild NPDR and 35 of 41 eyes (85%) with moderate NPDR progressed 2 steps or more were identified. All 4 eyes with mild NPDR that progressed within 6 months and 1 year were identified, and 8 of 9 (89%) and 17 of 20 (85%) with moderate NPDR that progressed within 6 months and 1 year, respectively, were identified.Conclusions and Relevance This study demonstrates the accuracy and feasibility of automated ML models for identifying DR progression developed using UWF images, especially for prediction of 2-step or greater DR progression within 1 year. Potentially, the use of ML algorithms may refine the risk of disease progression and identify those at highest short-term risk, thus reducing costs and improving vision-related outcomes.<br/