Imager Evaluation of Diabetic Retinopathy at the Time of Imaging in a Telemedicine Program

Objective: To evaluate the ability of certified retinal imagers to identify presence versus absence of sight-threatening diabetic retinopathy (stDR) (moderate nonproliferative diabetic retinopathy or worse or diabetic macular edema) at the time of retinal imaging in a telemedicine program. Research Design and Methods: Diabetic patients in a primary care setting or specialty diabetes clinic received Joslin Vision Network protocol retinal imaging as part of their care. Trained nonphysician imagers graded the presence versus absence of stDR at the time of imaging. These gradings were compared with masked gradings of certified readers. Results: Of 158 patients (316 eyes) imaged, all cases of stDR (42 eyes [13%]) were identified by the imagers at the time of imaging. Six eyes with mild nonproliferative diabetic retinopathy were graded by the imagers to have stDR (sensitivity 1.00, 95% CI 0.90–1.00; specificity 0.97, 0.94–0.99). Conclusions: Appropriately trained imagers can accurately identify stDR at the time of imaging

Ultrawide Field Imaging in Diabetic Retinopathy: Exploring the Role of Quantitative Metrics

Ultrawide field imaging (UWF) has allowed the visualization of a significantly greater area of the retina than previous standard approaches. In diabetic retinopathy (DR), significantly more lesions are seen on UWF imaging compared to the seven-standard ETDRS fields. In addition, some eyes have lesions that are located predominantly in the peripheral retina that are associated with an increased risk of DR progression. The current DR severity scales are still largely based on clinically visible retinal microvascular lesions and do not incorporate retinal periphery, neuroretinal, or pathophysiologic changes. Thus, current scales are not well suited for documenting progression or regression in eyes with very early or advanced DR, nor in the setting of vascular endothelial growth factor inhibitors (antiVEGF). In addition, the categorical system is highly subjective, and grading is variable between different graders based on experience level and training background. Recently, there have been efforts to quantify DR lesions on UWF imaging in an attempt to generate objective metrics for classification, disease prognostication and prediction of treatment response. The purpose of this review is to examine current quantitative metrics derived from UWF fluorescein angiograms and UWF color imaging to determine their feasibility in any potential future DR classification

Visual Acuity Testing Using Autorefraction or Pinhole Occluder Compared with a Manual Protocol Refraction in Individuals with Diabetes

Purpose: To compare visual acuity (VA) scores obtained after autorefraction or using a pinhole occluder to scores obtained after refraction according to a standard clinical research protocol. Design: Prospective, comparative case series Participants: One hundred and ten study participants (209 eyes) with diabetes mellitus and a broad range of diabetic retinopathy severity and visual acuity (VA). Methods: VA was measured after autorefraction by a Topcon KR-8000 autorefractor as well as after a Diabetic Retinopathy Clinical Research Network (DRCR.net) protocol manual refraction. The order of testing was randomized and examiners were masked to the source of each refraction. A second VA measurement, utilizing an identical DRCR.net manual refraction, was made in a subset of eyes (N = 144, 69%) in order to establish test-retest variability for comparison purposes. All eyes underwent VA testing using a pinhole occluder. Main Outcome Measures: Best corrected VA as measured by the Electronic Early Treatment Diabetic Retinopathy Study Visual Acuity Test© (E-ETDRS). Results: In all eyes, the median E-ETDRS VA letter score (EVA) obtained after manual refraction (MR-EVA) was 69 (Snellen equivalent 20/40), ranging from 4 to 93 (20/800 to 20/16). The median MR-EVA was slightly better than the median EVA obtained after autorefraction (AR-EVA), with a median difference (AR-EVA – MR-EVA) of −1 letter (25th, 75th percentiles: −4, 2 letters). The absolute difference between AR-EVA and MR-EVA was similar to the test-retest variability of MR-EVA alone. In contrast, MR-EVA was better than EVA obtained using a pinhole occluder (PH-EVA), (median PH-EVA – MR-EVA: −4 letters [−9, 0]), and had significantly less test-retest variability (P<0.001). Generally, the spherical equivalent of autorefraction was slightly more hyperopic (or less myopic) than the spherical equivalent of manual refraction (median difference: +0.25 Diopters [0, +0.63 Diopters]). Conclusions: Given the substantial time and effort required for training and certification of study protocol refractionists, and the similarity between AR-EVA and MR-EVA, further evaluation of autorefraction, but not pinhole occluder testing, as an alternative to the current clinical research gold standard of ETDRS protocol manual refraction in study participants with diabetic retinopathy is warranted

Effects of Dilation on Electronic-ETDRS Visual Acuity in Diabetic Patients

Objectives: To evaluate the effect of pupillary dilation on electronic-ETDRS visual acuity (EVA) in diabetic subjects and to assess post-dilation EVA as a surrogate for pre-dilation visual acuity (VA). Methods and Design: DRCR.net-protocol refraction and EVA were measured pre- and post-dilation in diabetic subjects by independent, masked examiners. Results: In 129 eyes of 66 subjects, median [25th, 75th percentiles] pre-dilation EVA score was 69 [54, 86] (Snellen-equivalent 20/40-1 [20/80-1, 20/20+1]). Pre-dilation VA was ≥20/20, 20/25-20/40, 20/50-20/80, and <20/80 in 29%, 19%, 26%, and 26% of eyes, respectively. Median EVA change post-dilation was -3 letters [-7, 0]. EVA change was ≥15 letters (≥ 3 ETDRS lines) in 9% of eyes and ≥10 letters (≥ 2 ETDRS lines) in 19% of eyes. Extent of change (range +12 to -25 letters) was associated with baseline VA. No relationship was identified between EVA change and gender, race, lens status, refractive error, DR severity, or primary cause of vision loss. Conclusions: In an optimized clinical trial setting, there is a decline in best-corrected EVA after dilation in diabetic subjects. The large range and magnitude of VA change preclude using post-dilation EVA as a surrogate for undilated VA

Disparities Between Teleretinal Imaging Findings and Patient-reported Diabetic Retinopathy Status and Followup Eye Care Interval: A 10-year Prospective study

Objective: To assess self-reported awareness of diabetic retinopathy (DR) and concordance of eye examination follow-up compared to findings from concurrent retinal images. Research Design and Methods: Prospective observational 10-year study of 26,876 consecutive patients with diabetes that underwent retinal imaging during an endocrinology visit. Awareness and concordance was evaluated using questionnaires and retinal imaging. Results: Awareness information and gradable images were available in 25,360 patients (94.3%). Severity of DR by imaging: no DR 14,317(56.5%); mild DR 6,805(26.8%), vision-threatening DR (vtDR) 4,238(16.7%). Patients did not report being aware of any prior DR in 96.7%, 88.5% and 54.9% (no,mild,vtDR). When DR was present, reporting no prior DR was associated with shorter diabetes duration, milder DR, last eye exam >1 year prior, no dilation, no scheduled appointment, and less specialized provider (all P<0.001). Among patients with vtDR, 41.2%, 58.1% and 64.2% did not report being aware of any DR and follow-up was concordant with current DR severity in 66.7%, 41.3% and 25.4% (P<0.001) when prior examination was performed by a retina specialist, non-retina ophthalmologist, or optometrist (P<0.001). Conclusions: Substantial discrepancies exist between DR presence, patient awareness and concordance of follow-up across all DR severity levels. These discrepancies are present across all eye care provider types with the magnitude influenced by provider type. Thus, patient self-report should not be relied upon to reflect DR status. Modification of medical care and education models may be necessary to enhance retention of ophthalmic knowledge in patients with diabetes and assure accurate communication between all health care providers.</p

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/