To determine the relationship between vitreous fluorophotometry (VF) and severity of diabetic retinopathy (DR) 13 patients with mild to moderate background DR starting continuous subcutaneous insulin infusion were followed up serially for 12 months. They were studied by colour stereo retinal photographs, which were assessed by the Wisconsin Grading System, and by VF, which was assessed by the permeability index of the retina for fluorescein. By four months the severity of DR on colour photographs had deteriorated by at least one level in at least one eye in eight patients. In four patients the DR did not deteriorate. Assessment of anatomical abnormalities by severity of DR on colour photographs correlated well with the functional abnormalities of the blood-retinal barrier(s) assessed by VF, especially for the macular field. Comparison of permeability index data in the patients developing preproliferative or proliferative features of DR (group A) with the same data in patients who did not develop such changes (group B) indicated that group A patients had more severe DR than did group B patients at entry. Grading of colour photographs showed a similar trend but with greater overlap. Considerable overlap in fluorescein permeability remained between those subjects with no visible DR and those with microaneurysms with or without haemorrhages and small hard exudates.
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http://dx.doi.org/10.1136/bjo.73.4.255 | DOI Listing |
Taiwan J Ophthalmol
November 2024
Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Thailand.
Recent advances of artificial intelligence (AI) in retinal imaging found its application in two major categories: discriminative and generative AI. For discriminative tasks, conventional convolutional neural networks (CNNs) are still major AI techniques. Vision transformers (ViT), inspired by the transformer architecture in natural language processing, has emerged as useful techniques for discriminating retinal images.
View Article and Find Full Text PDFTaiwan J Ophthalmol
December 2024
Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, Tamil Nadu, India.
The aim of this study is to describe genotype and phenotype of patients with bestrophinopathy. The case records were reviewed retrospectively, findings of multimodal imaging such as color fundus photograph, optical coherence tomography (OCT), fundus autofluorescence, electrophysiological, and genetic tests were noted. Twelve eyes of six patients from distinct Indian families with molecular diagnosis were enrolled.
View Article and Find Full Text PDFOphthalmology
January 2025
University of Bordeaux, INSERM, BPH, U1219, F-33000 Bordeaux, France; FRCRnet, F-CRIN network, France.
Purpose: We assessed the associations of macular layer thicknesses, measured using spectral-domain OCT (SD-OCT), with incident age-related macular degeneration (AMD) and AMD polygenic risk scores (PRS).
Design: Population-based cohort study PARTICIPANTS: 653 participants of the Alienor study, with biennial eye imaging from 2009 to 2024.
Methods: Macular layer thicknesses of eight distinct layers and three compound layers were automatically segmented based on SD-OCT imaging of the macula.
West J Nurs Res
January 2025
Golden Apple Healing Arts, LLC, Wauwatosa, WI, USA.
Background: The concept of inclusion within diversity, equity, and inclusion has broad meanings and implications and has not been explored in nursing through a World Café.
Purpose: To describe the process and experiences of 9 nurse scientists who hosted a World Café focused on inclusion, to share participants' insights, and to offer considerations to advance inclusion in nursing.
Approach: We hosted and encouraged active participation in a World Café that focused on 7 inclusion topics in nursing during the 2024 Midwestern Nursing Research Society Annual Research Conference.
Transl Vis Sci Technol
January 2025
Institute of the Electrical and Biomedical Engineering, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tyrol, Austria.
Purpose: To extract conjunctival bulbar redness from standardized high-resolution ocular surface photographs of a novel imaging system by implementing an image analysis pipeline.
Methods: Data from two trials (healthy; outgoing ophthalmic clinic) were collected, processed, and used to train a machine learning model for ocular surface segmentation. Various regions of interest were defined to globally and locally extract a redness biomarker based on color intensity.
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