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Skip search results from other journals and go to results- 4 JMIR Medical Informatics
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However, the sharp rise in diabetes cases, coupled with a shortage of trained retinal specialists and ophthalmologists [13], makes DRS services accessibility challenging, particularly as 80% of India’s older adult population resides in rural areas [10]. The COVID-19 pandemic posed additional significant challenges to health care systems worldwide, inevitably leading to the curtailment of health services accessibility, including DRS [14,15].
JMIR Form Res 2025;9:e67047
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Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinalretinal
JMIR Med Inform 2025;13:e58107
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EyeMatics: An Ophthalmology Use Case Within the German Medical Informatics Initiative
In this perspective paper, we present the cross-site and cross-state Eye Matics approach, which enhances ophthalmic research by connecting previously isolated subsystems, such as retinal scans through optical coherence tomography (OCT), clinical assessment data, and patient-reported outcomes (PROs). Moreover, we ensure sustainable data exchange by consistently utilizing international interoperability standards.
JMIR Med Inform 2024;12:e60851
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Retinal diseases are the main afflictions affecting human vision. Diabetic retinopathy (DR) is an eye vascular disease caused by diabetes [1]. Following DR, retinal vein occlusion is the most frequent retinal vascular disorder [2]. Drusen, long-spaced collagen, and phospholipid vesicles are all linked to age-related macular degeneration (AMD). These structures exist between the retinal pigment epithelium’s basement membrane and the rest of the Bruch membrane [3].
J Med Internet Res 2022;24(6):e37532
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In addition to retinal fundus images for identifying diabetic retinopathy, AMD, and glaucoma [7], a deep learning model using OCT for retinal layer segmentation and retinal disease identification was developed by the Deep Mind group [8]. Moreover, deep learning could help to detect ischemic zones in retinal vascular diseases through the use of ultra-wide-field FA [25]. The aforementioned studies demonstrated that deep learning can be effectively applied for a single retinal imaging modality.
JMIR Med Inform 2021;9(5):e28868
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In Figure 3, the retinal-vessel features are marked for a true-positive case with a relatively normal retinal fundus image. Common signs of retina abnormality, such as exudation, hemorrhage, and drusen, also played a role in the detection of renal function impairment.
Selected retinal fundus images and their corresponding saliency maps in true-negative and true-positive cases. (A) No renal function impairment detected. Patient’s e GFR = 102.6 m L/min/1.73 m2 and Hb A1c = 13.4%.
JMIR Med Inform 2020;8(11):e23472
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