Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites because of cost and workflow constraints. Instead, screening programs rely on the detection of hard exudates in color fundus photographs as a proxy for DME, often resulting in high false positive or false negative calls. To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME. Our model had an ROC-AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, three retinal specialists had similar sensitivities (82-85%), but only half the specificity (45-50%, p<0.001 for each comparison with model). The positive predictive value (PPV) of the model was 61% (95% CI: 56-66%), approximately double the 36-38% by the retinal specialists. In addition to predicting ci-DME, our model was able to detect the presence of intraretinal fluid with an AUC of 0.81 (95% CI: 0.81-0.86) and subretinal fluid with an AUC of 0.88 (95% CI: 0.85-0.91). The ability of deep learning algorithms to make clinically relevant predictions that generally require sophisticated 3D-imaging equipment from simple 2D images has broad relevance to many other applications in medical imaging

Birdshot retinochoroidopathy review

Birdshot retinochoroidopathy (BSRC) is a distinct type of posterior uveitis originally described in the 1940s. Its characteristics include minimal anterior segment inflammation and diffuse posterior choroidopathy with vitritis and retinal vasculitis. The precise etiology of this disease is yet to be elucidated. However, various treatment modalities have been employed with the ultimate goal of durable remission of this vision threatening intraocular disease. The purpose of this review is not only to emphasize the importance of recognizing BSRC, but also to discuss the new discoveries, immune mediators, current and new therapies, and techniques applied to monitor and accomplish disease remission

The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation

We compared the performance of deep learning (DL) in the classification of optical coherence tomography (OCT) images of macular diseases between automated classification alone and in combination with automated segmentation. OCT images were collected from patients with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, diabetic macular edema, retinal vein occlusion, cystoid macular edema in Irvine-Gass syndrome, and other macular diseases, along with the normal fellow eyes. A total of 14,327 OCT images were used to train DL models. Three experiments were conducted: classification alone (CA), use of automated segmentation of the OCT images by RelayNet, and the graph-cut technique before the classification (combination method 1 (CM1) and 2 (CM2), respectively). For validation of classification of the macular diseases, the sensitivity, specificity, and accuracy of CA were found at 62.55%, 95.16%, and 93.14%, respectively, whereas the sensitivity, specificity, and accuracy of CM1 were found at 72.90%, 96.20%, and 93.92%, respectively, and of CM2 at 71.36%, 96.42%, and 94.80%, respectively. The accuracy of CM2 was statistically higher than that of CA (p = 0.05878). All three methods achieved AUC at 97%. Applying DL for segmentation of OCT images prior to classification of the images by another DL model may improve the performance of the classification

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding&nbsp;to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82-85%), but only half the specificity (45-50%, p &lt; 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81-0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85-0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment&nbsp;and with better than specialist performance, has broad relevance to many other applications in medical imaging

Current practice in the management of ocular toxoplasmosis

Background Ocular toxoplasmosis is common across all regions of the world. Understanding of the epidemiology and approach to diagnosis and treatment have evolved recently. In November 2020, an international group of uveitis-specialised ophthalmologists formed the International Ocular Toxoplasmosis Study Group to define current practice. Methods 192 Study Group members from 48 countries completed a 36-item survey on clinical features, use of investigations, indications for treatment, systemic and intravitreal treatment with antiparasitic drugs and corticosteroids, and approach to follow-up and preventive therapy. Results For 77.1% of members, unilateral retinochoroiditis adjacent to a pigmented scar accounted for over 60% of presentations, but diverse atypical presentations were also reported. Common complications included persistent vitreous opacities, epiretinal membrane, cataract, and ocular hypertension or glaucoma. Most members used clinical examination with (56.8%) or without (35.9%) serology to diagnose typical disease but relied on intraocular fluid testing-usually PCR-in atypical cases (68.8%). 66.1% of members treated all non-pregnant patients, while 33.9% treated selected patients. Oral trimethoprim-sulfamethoxazole was first-line therapy for 66.7% of members, and 60.9% had experience using intravitreal clindamycin. Corticosteroid drugs were administered systemically by 97.4%; 24.7% also injected corticosteroid intravitreally, almost always in combination with an antimicrobial drug (72.3%). The majority of members followed up all (60.4%) or selected (35.9%) patients after resolution of acute disease, and prophylaxis against recurrence with trimethoprim-sulfamethoxazole was prescribed to selected patients by 69.8%. Conclusion Our report presents a current management approach for ocular toxoplasmosis, as practised by a large international group of uveitis-specialised ophthalmologists