32 research outputs found

    A Case of Peripheral Ulcerative Keratitis Associated with Autoimmune Hepatitis

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    Purpose. To describe a case of peripheral ulcerative keratitis in the setting of autoimmune hepatitis and possible overlap syndrome with primary sclerosing cholangitis. Case Report. A 48-year-old African American female with autoimmune hepatitis with possible overlap syndrome with primary sclerosing cholangitis presented with tearing, irritation, and injection of the left eye that was determined to be peripheral ulcerative keratitis. The patient was treated with topical and systemic steroids, immunosuppressant drugs (azathioprine and mycophenolate mofetil), a biologic (rituximab), and surgery (conjunctival resection), and the peripheral ulcerative keratitis epithelialized but ultimately led to corneal perforation. Conclusion. In this unique case, a patient with peripheral ulcerative keratitis who underwent treatment ultimately had a corneal perforation. This case may suggest a possible relationship between autoimmune hepatitis and peripheral ulcerative keratitis

    Topical Bevacizumab in the Treatment of Corneal Neovascularization

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    Effect of simulated cataract on the accuracy of artificial intelligence in detecting diabetic retinopathy in color fundus photos

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    Purpose: Artificial intelligence (AI) is often trained on images without ocular co-morbidities, limiting its generalizability. This study aims to evaluate the accuracy of a convolutional neural network (CNN) applied to color fundus photos (CFPs) with simulated cataracts (SCs) in detecting diabetic retinopathy (DR). Methods: A database of 3662 CFPs (from Asia Pacific Tele-Ophthalmology Society (APTOS) 2019) was used. Using transfer learning, a CNN was trained to classify the training images as either DR or non-DR. The CNN was then applied to classify the testing images after an SC was applied, using varying degrees of Gaussian blur. Results: Accuracy without SC was 97.0%, sensitivity (Sn) 95.7%, specificity (Sp) 98.3%. For mild SC, accuracy was 93.1%, Sn 91.8%, Sp 94.3%. For moderate SC, accuracy was 62.8%, Sn 31.4%, Sp 95.2%. For severe SC, accuracy was 53.5%, Sn 11.8%, Sp 96.5%. Conclusion: SCs significantly impaired AI accuracy. To prepare AI for clinical use, cataracts and other real-world clinical challenges affecting image quality must be accounted for
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