Barriers and Facilitators to Ophthalmology Visit Adherence in an Urban Hospital Setting.

Purpose: To explore barriers and facilitators to completing scheduled outpatient appointments at an urban academic hospital-based ophthalmology department. Methods: Potential participants were stratified by neighborhood Social Vulnerability Index (SVI) (range, 0-1.0, higher scores indicate greater vulnerability), and semistructured interviews were conducted with individuals 18 years and older with an SVI of greater than 0.61 (n = 17) and providers delivering care in the General Eye Clinic of the University of Illinois Chicago (n = 8). Qualitative analysis informed by human-centered design methods was conducted to classify barriers and facilitators into three domains of the Consolidated Framework for Implementation Research: outer setting, inner setting, and characteristics of individuals. Results: There were four main themes-transportation, time burden, social support, and economic situation-all of which were within the outer setting of the Consolidated Framework for Implementation Research; transportation was most salient. Although providers perceived health literacy as a barrier affecting motivation, patients expressed a high motivation to attend visits and felt well-educated about their condition. Conclusions: A lack of resources outside of the health system presents significant barriers for patients from neighborhoods with high SVI. Future efforts to improve adherence should focus on resource-related interventions in the outer setting. Improving access to eye care will require community-level interventions, particularly transportation. Translational Relevance: Understanding the barriers and facilitators within the Consolidated Framework for Implementation Research provides useful guidance for future interventions, specifically to focus future efforts to improve adherence on resource-related interventions.

Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy.

Purpose: To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy. Methods: A deep-learning convolutional neural network (CNN) architecture, VGG16, was employed for this study. A transfer learning process was implemented to retrain the CNN for robust OCTA classification. One dataset, consisting of images of 32 healthy eyes, 75 eyes with diabetic retinopathy (DR), and 24 eyes with diabetes but no DR (NoDR), was used for training and cross-validation. A second dataset consisting of 20 NoDR and 26 DR eyes was used for external validation. To demonstrate the feasibility of using artificial intelligence (AI) screening of DR in clinical environments, the CNN was incorporated into a graphical user interface (GUI) platform. Results: With the last nine layers retrained, the CNN architecture achieved the best performance for automated OCTA classification. The cross-validation accuracy of the retrained classifier for differentiating among healthy, NoDR, and DR eyes was 87.27%, with 83.76% sensitivity and 90.82% specificity. The AUC metrics for binary classification of healthy, NoDR, and DR eyes were 0.97, 0.98, and 0.97, respectively. The GUI platform enabled easy validation of the method for AI screening of DR in a clinical environment. Conclusions: With a transfer learning process for retraining, a CNN can be used for robust OCTA classification of healthy, NoDR, and DR eyes. The AI-based OCTA classification platform may provide a practical solution to reducing the burden of experienced ophthalmologists with regard to mass screening of DR patients. Translational Relevance: Deep-learning-based OCTA classification can alleviate the need for manual graders and improve DR screening efficiency.

QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY.

PURPOSE: This study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging. METHODS: One hundred and twenty OCTA images from 60 NPDR (mild, moderate, and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all the OCTAs were 6 mm × 6 mm macular scans. Six quantitative features, that is, blood vessel tortuosity, blood vascular caliber, vessel perimeter index, blood vessel density, foveal avascular zone area, and foveal avascular zone contour irregularity (FAZ-CI) were derived from each OCTA image. A support vector machine classification model was trained and tested for computer-aided classification of NPDR stages. Sensitivity, specificity, and accuracy were used as performance metrics of computer-aided classification, and receiver operation characteristics curve was plotted to measure the sensitivity-specificity tradeoff of the classification algorithm. RESULTS: Among 6 individual OCTA features, blood vessel density shows the best classification accuracies, 93.89% and 90.89% for control versus disease and control versus mild NPDR, respectively. Combined feature classification achieved improved accuracies, 94.41% and 92.96%, respectively. Moreover, the temporal-perifoveal region was the most sensitive region for early detection of DR. For multiclass classification, support vector machine algorithm achieved 84% accuracy. CONCLUSION: Blood vessel density was observed as the most sensitive feature, and temporal-perifoveal region was the most sensitive region for early detection of DR. Quantitative OCTA analysis enabled computer-aided identification and staging of NPDR.