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| http://dx.doi.org/10.1186/s12958-024-01308-5 | DOI Listing |
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Perfusion Capacity as a Predictive Index for Assessing Visual Functional Recovery in Patients With Idiopathic Epiretinal Membrane.
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State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
Purpose: This study investigates the association between visual function and retinal vasculature metrics, particularly perfusion capacity (PC), in eyes with idiopathic epiretinal membrane (iERM), using optical coherence tomography angiography (OCTA).
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Optimising Instrumented Mouthguard Data Analysis: Video Synchronisation Using a Cross-correlation Approach.
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Carnegie Applied Rugby Research (CARR) Centre, Carnegie School of Sport, Leeds Beckett University, Leeds, UK.
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Department of Nursing, China Medical University, Shenyang, Liaoning, China.
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Using near-infrared hyperspectral imaging combined with machine learning to predict the components and the origin of Radix Paeoniae Rubra.
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View Article and Find Full Text PDFOcular Biometry OCR: a machine learning algorithm leveraging optical character recognition to extract intra ocular lens biometry measurements.
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School of Medicine, Stanford University, Palo Alto, CA, United States.
Given close relationships between ocular structure and ophthalmic disease, ocular biometry measurements (including axial length, lens thickness, anterior chamber depth, and keratometry values) may be leveraged as features in the prediction of eye diseases. However, ocular biometry measurements are often stored as PDFs rather than as structured data in electronic health records. Thus, time-consuming and laborious manual data entry is required for using biometry data as a disease predictor.
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