Characteristics of Bilateral Retinal Detachment.

INTRODUCTION: Rhegmatogenous retinal detachment (RD) is still a sight-threatening and potentially blinding disease, especially if both eyes are affected. The purpose of this study is analysing the specific characteristics of bilateral rhegmatogenous RD. METHODS: The files of all 5,791 consecutive eyes undergoing vitreoretinal surgery for uncomplicated RD in a single tertiary retinal centre between January 2005 and June 2021 were retrospectively reviewed. RESULTS: A total of 300 patients (600 eyes) had bilateral retinal detachment. Interval between initial and subsequent RD surgery was 2.6 ± 2.8 (mean ± SD, median 1.5) years. Symptoms were reported by the patients for 20 ± 75 (median 5) days before presentation in the initial eye and 12 ± 32 (median 4) days in the subsequent eye. 220 patients were male (73%), and mean age at initial RD was 55 years. 183 (61%) of the initial RD eyes were phakic. In the initial eye, more patients had a detached macula, worse visual acuity, and more quadrants involved. Primary anatomic success rate was higher in the subsequent eye (90%) compared to the initial eye (83%). There was no difference in the reattachment rate of fellow eyes with primary failure in the first eye (91%) compared to those with primary success in the first eye (90%). There was a high symmetry between the eyes in terms of type of retinal break, number of breaks, and presumed localization of the causative retinal break. CONCLUSION: Patients with bilateral RD were more commonly male and younger than the group of all RD patients. The proportion of pseudophakia was not different. The majority of fellow eye RD occurred within 2 years after the RD in the first eye. Second eye RD was less advanced and had a better anatomical repair rate. Despite their experience in the first eye and despite typical symptoms, patients presented only after a mean of 12 days with RD in the second eye. RD in the initial and the subsequent eye showed a high symmetry. The anatomic result in the first eye is not a predictor for the anatomic result in the subsequent eye.

OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications.

PURPOSE: Intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF) medications have become the standard of care for their respective indications. Optical coherence tomography (OCT) scans of the central retina provide detailed anatomical data and are widely used by clinicians in the decision-making process of anti-VEGF indication. In recent years, significant progress has been made in artificial intelligence and computer vision research. We trained a deep convolutional artificial neural network to predict treatment indication based on central retinal OCT scans without human intervention. METHOD: A total of 183,402 retinal OCT B-scans acquired between 2008 and 2016 were exported from the institutional image archive of a university hospital. OCT images were cross-referenced with the electronic institutional intravitreal injection records. OCT images with a following intravitreal injection during the first 21 days after image acquisition were assigned into the 'injection' group, while the same amount of random OCT images without intravitreal injections was labeled as 'no injection'. After image preprocessing, OCT images were split in a 9:1 ratio to training and test datasets. We trained a GoogLeNet inception deep convolutional neural network and assessed its performance on the validation dataset. We calculated prediction accuracy, sensitivity, specificity, and receiver operating characteristics. RESULTS: The deep convolutional neural network was successfully trained on the extracted clinical data. The trained neural network classifier reached a prediction accuracy of 95.5% on the images in the validation dataset. For single retinal B-scans in the validation dataset, a sensitivity of 90.1% and a specificity of 96.2% were achieved. The area under the receiver operating characteristic curve was 0.968 on a per B-scan image basis, and 0.988 by averaging over six B-scans per examination on the validation dataset. CONCLUSION: Deep artificial neural networks show impressive performance on classification of retinal OCT scans. After training on historical clinical data, machine learning methods can offer the clinician support in the decision-making process. Care should be taken not to mistake neural network output as treatment recommendation and to ensure a final thorough evaluation by the treating physician.