Computational Ophthalmology – Page 10 – big data. machine learning. data science.

June 23, 2017August 7, 2020

Deep-learning based, automated segmentation of macular edema in optical coherence tomography

In this study, the authors developed a convolutional neural network that successfully detected intraretinal fluid on ocular coherence tomography images. Macular edema, characterized by loss of the blood retinal barrier in very small retinal blood vessels, leads to the accumulation of fluid in the retina that can interfere with vision. The degree of macula edema present is usually inferred by measuring central retinal thickness on ocular coherence tomography images, but this measurement does not fully capture the extent and severity of the intraretinal fluid present.

May 29, 2017September 27, 2020

The United Kingdom Diabetic Retinopathy Electronic Medical Record Users Group: Report 3: Baseline Retinopathy and Clinical Features Predict Progression of Diabetic Retinopathy

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In this large study using data from more than 30,000 patients in the UK, the authors analyzed potential risk factors for developing proliferative diabetic retinopathy and vitreous hemorrhage in patients with diabetes mellitus. Diabetic retinopathy is a major cause of blindness in working-age adults, and early detection can substantially decrease the risk of progression to vision loss. Patients with diabetes should have regular screening for early signs of diabetic retinopathy, but the current severity classification systems may not be the most accurate method for predicting which patients are most at risk for vision-threatening disease, particularly as standards of care have been updated.

March 1, 2017September 27, 2020

Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records

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In this study, the authors created and tested an algorithm for extracting visual acuity testing information from clinical notes. The increasing use of electronic health records is allowing for large-scale data review in clinical research. The data accessed from electronic health records can be limited, however, because the manually transcribed data is often stored as free text. Automated conversion of free text into machine-readable data and extraction for research requires the application of computational linguistics and natural language processing.