Diabetic retinopathy is a leading cause of vision loss in Canada and creates significant economic and social burden on patients. Diabetic retinopathy is largely a preventable complication of diabetes mellitus. Yet, hundreds of thousands of Canadians continue to be at risk and thousands go on to develop vision loss and disability. Blindness has a significant impact on the Canadian economy, on families and the quality of life of affected individuals. This paper provides an economic analysis on two potential interventions for preventing blindness and concludes that use of AI to identify high-risk individuals could significantly decrease the costs of identifying, recalling, and screening patients at risk of vision loss, while achieving similar results as a full-fledged screening and recall program. We propose that minimal data interoperability between optometrists and family physicians combined with artificial intelligence to identify and screen those at highest risk of vision loss can lower the costs and increase the feasibility of screening and treating large numbers of patients at risk of going blind in Canada.
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http://dx.doi.org/10.3233/SHTI231317 | DOI Listing |
Heliyon
January 2025
Genetics, Biotechnology and Seed Science Unit (GBioS), Laboratory of Crop Production, Physiology and Plant Breeding, Faculty of Agronomic Sciences, University of Abomey-Calavi, 01 BP 526 Tri Postal Cotonou, Benin.
Regularly consuming orange-fleshed sweet potatoes (OFSP) is widely recognised as an effective way to treat vitamin A deficiency (VAD), particularly in low-income countries. Unfortunately, cultivars of OFSP are poorly disseminated in most countries in sub-Saharan Africa, where VAD is a major cause of blindness. This study was conducted to evaluate the effect of the genotype-environment interaction (GEI) on the performance and stability of the yield components of OFSP cultivars to trigger their adoption by farmers.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
January 2025
No. 1 Teaching Hospital, Norman Bethune College of Medicine, Jilin University, Changchun, Jilin, China.
Thyroid-associated ophthalmopathy (TAO), an autoimmune disease closely related to thyroid dysfunction, remains a challenging ophthalmic condition among adults. Its clinical manifestations are complex and diverse, and disease progression can lead to exophthalmos, diplopia, exposure keratitis, corneal ulceration, and compressive optic neuropathy, resulting in irreversible vision damage or even blindness. Traditional treatment methods for TAO, including glucocorticoids, immunosuppressants, and radiation therapy, often have limitations and side effects, making this disease problematic in ophthalmology.
View Article and Find Full Text PDFJ Cell Sci
January 2025
Program in Molecular Medicine, University of Massachusetts Chan Medical School, Suite 213 Biotech II, 373 Plantation Street, Worcester MA 01605, USA.
In humans, inositol polyphosphate-5-phosphatase e (INPP5E) mutations cause retinal degeneration as part of Joubert and MORM syndromes and can also cause non-syndromic blindness. In mice, mutations cause a spectrum of brain, kidney, and other anomalies and prevent the formation of photoreceptor outer segments. To further explore the function of Inpp5e in photoreceptors, we generated conditional and inducible knockouts of mouse Inpp5e where the gene was deleted either during outer segment formation or after outer segments were fully formed.
View Article and Find Full Text PDFBMC Ophthalmol
January 2025
Department of Ophthalmology, Peking University Third Hospital, Beijing, China.
Background: Fungal keratitis can develop after plant injury or after prolonged glucocorticoid use. Typical manifestations include corneal infiltrates, satellite lesions, plaques, and an immune ring. Some cases exhibit atypical signs, requiring reliance on etiological examination.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
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