PurposeTo determine if final visual acuity (VA) is affected by duration of macular detachment (DMD) within the first week of macula-off retinal detachment (RD).MethodsThis is a retrospective study of eyes that underwent repair within 7 days with vitrectomy or vitrectomy with scleral buckle for macula-off RD at Stanford University Hospital between 1 May 2007 and 1 May 2017. A generalised linear model was constructed using DMD, postoperative lens status, preoperative VA, patient age and surgeon as the independent variables and the final VA as the dependent variable. The main outcome measure was the final VA.ResultsSeventy-nine eyes met the entry criteria. Group 1 included 52 eyes with RD repaired within 3 days of DMD, and group 2 included 27 eyes repaired between 4 and 7 days of DMD. The average final VA in group 1 eyes was logarithm of the minimum angle of resolution (logMAR) 0.21 (Snellen 20/33) and in group 2 eyes was logMAR 0.54 (Snellen 20/69). In group 1 and group 2 eyes, preoperative VA (p=0.017and p=0.007), DMD (p=0.004 and p=0.041) and final lens status (p<0.0001 and p<0.001) predicted postoperative VA. Post-hoc analysis showed significant differences in final VA between detachments of 1day vs 3 days (p=0.0009).ConclusionDMD affects the final VA even among patients whose DMD is <3 days. Based on these results, interventions that shorten DMD, including those occurring within the first 3days, may result in improved long-term VA outcomes.
PurposeTo introduce a novel method to segment individual drusen in spectral-domain optical coherence tomography (SD-OCT), and evaluate its accuracy, and repeatability/reproducibility of drusen quantifications extracted from the segmentation results.MethodsOur method uses a smooth interpolation of the retinal pigment epithelium (RPE) outer boundary, fitted to candidate locations in proximity to Bruch's Membrane, to identify regions of substantial lifting in the inner-RPE or inner-segment boundaries, and then separates and evaluates individual druse independently. The study included 192 eyes from 129 patients. Accuracy of drusen segmentations was evaluated measuring the overlap ratio (OR) with manual markings, also comparing the results to a previously proposed method. Repeatability and reproducibility across scanning protocols of automated drusen quantifications were investigated in repeated SD-OCT volume pairs and compared with those measured by a commercial tool (Cirrus HD-OCT).ResultsOur segmentation method produced higher accuracy than a previously proposed method, showing similar differences to manual markings (0.72 ± 0.09 OR) as the measured intra- and interreader variability (0.78 ± 0.09 and 0.77 ± 0.09, respectively). The automated quantifications displayed high repeatability and reproducibility, showing a more stable behavior across scanning protocols in drusen area and volume measurements than the commercial software. Measurements of drusen slope and mean intensity showed significant differences across protocols.ConclusionAutomated drusen outlines produced by our method show promising accurate results that seem relatively stable in repeated scans using the same or different scanning protocols.Translational RelevanceThe proposed method represents a viable tool to measure and track drusen measurements in early or intermediate age-related macular degeneration patients.
Purpose To evaluate the performance of a deep learning algorithm in the detection of referral-warranted diabetic retinopathy (RDR) on low-resolution fundus images acquired with a smartphone and indirect ophthalmoscope lens adapter. Methods An automated deep learning algorithm trained on 92,364 traditional fundus camera images was tested on a dataset of smartphone fundus images from 103 eyes acquired from two previously published studies. Images were extracted from live video screenshots from fundus examinations using a commercially available lens adapter and exported as a screenshot from live video clips filmed at 1080p resolution. Each image was graded twice by a board-certified ophthalmologist and compared to the output of the algorithm, which classified each image as having RDR (moderate nonproliferative DR or worse) or no RDR. Results In spite of the presence of multiple artifacts (lens glare, lens particulates/smudging, user hands over the objective lens) and low-resolution images achieved by users of various levels of medical training, the algorithm achieved a 0.89 (95% confidence interval [CI] 0.83–0.95) area under the curve with an 89% sensitivity (95% CI 81%–100%) and 83% specificity (95% CI 77%–89%) for detecting RDR on mobile phone acquired fundus photos. Conclusions The fully data-driven artificial intelligence-based grading algorithm herein can be used to screen fundus photos taken from mobile devices and identify with high reliability which cases should be referred to an ophthalmologist for further evaluation and treatment. Translational Relevance The implementation of this algorithm on a global basis could drastically reduce the rate of vision loss attributed to DR.
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