Comparison of Diabetic Retinopathy Lesions Identified Using Ultrawide Field Imaging and Optical Coherence Tomography Angiography.

INTRODUCTION: Optical coherence tomography (OCT) angiography (OCTA) has the potential to influence the diagnosis and management of diabetic eye disease. This study aims to determine the correlation between diabetic retinopathy (DR) findings on ultrawide field (UWF) color photography (UWF-CP), UWF fluorescein angiography (UWF-FA), and OCTA. METHODS: This is a cross-sectional, prospective study. One hundred and fourteen eyes from 57 patients with diabetes underwent mydriatic UWF-CP, UWF-FA, and OCTA. DR severity was assessed. Ischemic areas were identified on UWF-FA using ImageJ and the nonperfusion index (NPI) was calculated. Diabetic macular edema (DME) was assessed using OCT. Superficial capillary plexus vessel density (VD), vessel perfusion (VP), and foveal avascular zone (FAZ) area were automatically measured on OCTA. Pearson correlation coefficient between the imaging modalities was determined. RESULTS: Forty-five eyes were excluded due to non-DR findings or prior laser photocoagulation; 69 eyes were analyzed. DR severity was associated with larger NPI (r = 0.55944, p < 0.0001) even after distinguishing between cones (Cone Nonperfusion Index [CPI]: r = 0.55617, p < 0.0001) and rods (Rod Nonperfusion Index [RPI]: r = 0.55285, p < 0.0001). In eyes with nonproliferative DR (NPDR), NPI is correlated with DME (r = 0.51156, p = 0.0017) and central subfield thickness (CST) (r = 0.67496, p < 0.0001). UWF-FA macular nonperfusion correlated with NPI (r = 0.42899, p = 0.0101), CPI (r = 0.50028, p = 0.0022), and RPI (r = 0.49027, p = 0.0028). Central VD and VP correlated with the DME presence (r = 0.52456, p < 0.0001; r = 0.51952, p < 0.0001) and CST (r = 0.50133, p < 0.0001; r = 0.48731, p < 0.0001). Central VD and VP were correlated with macular nonperfusion (r = 0.44503, p = 0.0065; r = 0.44239, p = 0.0069) in eyes with NPDR. Larger FAZ was correlated with decreased central VD (r = -0.60089, p = 0.0001) and decreased central VP (r = -0.59224, p = 0.0001). CONCLUSION: UWF-CP, UWF-FA, and OCTA findings provide relevant clinical information on diabetic eyes. Nonperfusion on UWF-FA is correlated with DR severity and DME. OCTA metrics of the superficial capillary plexus correlate with the incidence of DME and macular ischemia.

Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification from Multi-field Handheld Retinal Images.

PURPOSE: To create and validate code-free automated deep learning models (AutoML) for diabetic retinopathy (DR) classification from handheld retinal images. DESIGN: Prospective development and validation of AutoML models for DR image classification. PARTICIPANTS: A total of 17 829 deidentified retinal images from 3566 eyes with diabetes, acquired using handheld retinal cameras in a community-based DR screening program. METHODS: AutoML models were generated based on previously acquired 5-field (macula-centered, disc-centered, superior, inferior, and temporal macula) handheld retinal images. Each individual image was labeled using the International DR and diabetic macular edema (DME) Classification Scale by 4 certified graders at a centralized reading center under oversight by a senior retina specialist. Images for model development were split 8-1-1 for training, optimization, and testing to detect referable DR ([refDR], defined as moderate nonproliferative DR or worse or any level of DME). Internal validation was performed using a published image set from the same patient population (N = 450 images from 225 eyes). External validation was performed using a publicly available retinal imaging data set from the Asia Pacific Tele-Ophthalmology Society (N = 3662 images). MAIN OUTCOME MEASURES: Area under the precision-recall curve (AUPRC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 scores. RESULTS: Referable DR was present in 17.3%, 39.1%, and 48.0% of the training set, internal validation, and external validation sets, respectively. The model's AUPRC was 0.995 with a precision and recall of 97% using a score threshold of 0.5. Internal validation showed that SN, SP, PPV, NPV, accuracy, and F1 scores were 0.96 (95% confidence interval [CI], 0.884-0.99), 0.98 (95% CI, 0.937-0.995), 0.96 (95% CI, 0.884-0.99), 0.98 (95% CI, 0.937-0.995), 0.97, and 0.96, respectively. External validation showed that SN, SP, PPV, NPV, accuracy, and F1 scores were 0.94 (95% CI, 0.929-0.951), 0.97 (95% CI, 0.957-0.974), 0.96 (95% CI, 0.952-0.971), 0.95 (95% CI, 0.935-0.956), 0.97, and 0.96, respectively. CONCLUSIONS: This study demonstrates the accuracy and feasibility of code-free AutoML models for identifying refDR developed using handheld retinal imaging in a community-based screening program. Potentially, the use of AutoML may increase access to machine learning models that may be adapted for specific programs that are guided by the clinical need to rapidly address disparities in health care delivery. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Comparison of Handheld Retinal Imaging with ETDRS 7-Standard Field Photography for Diabetic Retinopathy and Diabetic Macular Edema.

PURPOSE: To compare nonmydriatic (NM) and mydriatic (MD) handheld retinal imaging with standard ETDRS 7-field color fundus photography (ETDRS photographs) for the assessment of diabetic retinopathy (DR) and diabetic macular edema (DME). DESIGN: Prospective, comparative, instrument validation study. SUBJECTS: A total of 225 eyes from 116 patients with diabetes mellitus. METHODS: Following a standardized protocol, NM and MD images were acquired using handheld retinal cameras (NM images: Aurora, Smartscope, and RetinaVue-700; MD images: Aurora, Smartscope, RetinaVue-700, and iNview) and dilated ETDRS photographs. Grading was performed at a centralized reading center using the International Clinical Classification for DR and DME. Kappa statistics (simple [K], weighted [Kw]) assessed the level of agreement for DR and DME. Sensitivity and specificity were calculated for any DR, referable DR (refDR), and vision-threatening DR (vtDR). MAIN OUTCOME MEASURES: Agreement for DR and DME; sensitivity and specificity for any DR, refDR, and vtDR; ungradable rates. RESULTS: Severity by ETDRS photographs: no DR, 33.3%; mild nonproliferative DR, 20.4%; moderate DR, 14.2%; severe DR, 11.6%; proliferative DR, 20.4%; no DME, 68.0%; DME, 9.3%; non-center involving clinically significant DME, 4.9%; center-involving clinically significant DME, 12.4%; and ungradable, 5.3%. For NM handheld retinal imaging, Kw was 0.70 to 0.73 for DR and 0.76 to 0.83 for DME. For MD handheld retinal imaging, Kw was 0.68 to 0.75 for DR and 0.77 to 0.91 for DME. Thresholds for sensitivity (0.80) and specificity (0.95) were met by NM images acquired using Smartscope and MD images acquired using Aurora and RetinaVue-700 cameras for any DR and by MD images acquired using Aurora and RetinaVue-700 cameras for refDR. Thresholds for sensitivity and specificity were met by MD images acquired using Aurora and RetinaVue-700 for DME. Nonmydriatic and MD ungradable rates for DR were 15.1% to 38.3% and 0% to 33.8%, respectively. CONCLUSIONS: Following standardized protocols, NM and MD handheld retinal imaging devices have substantial agreement levels for DR and DME. With mydriasis, not all handheld retinal imaging devices meet standards for sensitivity and specificity in identifying any DR and refDR. None of the handheld devices met the established 95% specificity for vtDR, suggesting that lower referral thresholds should be used if handheld devices must be utilized. When using handheld devices, the ungradable rate is significantly reduced with mydriasis and DME sensitivity thresholds are only achieved following dilation.