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

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 to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82–85%), but only half the specificity (45–50%, p < 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 and with better than specialist performance, has broad relevance to many other applications in medical imaging

Catching the therapeutic window of opportunity in early initial-onset Vogt�Koyanagi�Harada uveitis can cure the disease

Purpose: Vogt�Koyanagi�Harada (VKH) disease is a primary autoimmune granulomatous choroiditis that begins in the choroidal stroma. The aim of this review was to gather a body of evidence for the concept of a window of therapeutic opportunity, defined as a time interval following initial-onset disease during which adequate treatment will substantially modify the disease outcome and possibly even lead to cure, similar to what has been described for rheumatoid arthritis. Methods: We reviewed the literature and consulted leading experts in VKH disease to determine the consensus for the notion of a therapeutic window of opportunity in VKH disease. Results: We found a substantial body of evidence in the literature that a therapeutic window of opportunity exists for initial-onset acute uveitis associated with VKH disease. The disease outcome can be substantially improved if dual systemic steroidal and non-steroidal immunosuppressants are given within 2�3 weeks of the onset of initial VKH disease, avoiding evolution to chronic disease and development of �sunset glow fundus.� Several studies additionally report series in which the disease could be cured, using such an approach. Conclusions: There is substantial evidence for a therapeutic window of opportunity in initial-onset acute VKH disease. Timely and adequate treatment led to substantial improvement of disease outcome and prevented chronic evolution and �sunset glow fundus,� and very early treatment led to the cure after discontinuation of therapy in several series, likely due to the fact that the choroid is the sole origin of inflammation in VKH disease. © 2018 The Author(s

Erratum to: Changes in patterns of uveitis at a tertiary referral center in Northern Italy: analysis of 990 consecutive cases

Erratum to: Changes in patterns of uveitis at a tertiary referral center in Northern Italy: analysis of 990 consecutive case

Phototherapy in dermatology: A call for action

Of the wide range of treatment modalities available to dermatologists, few possess the history, efficacy, and safety of phototherapy. It should be emphasized that dermatologists are the only group of physicians optimally trained and qualified to understand the medical indications of phototherapy. Phototherapy, recognized for its cost-effectiveness, should remain a consideration in patient treatment. Continued training and education in residency and thereafter is needed to maintain the proficiency of physicians. In addition, payors need continued education to ensure that insurance coverage of phototherapy is not a barrier for patients to access this therapy. To further improve and optimize the outcome, phototherapy research needs to be supported