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Background/purpose: Dyslipidemia, a hallmark of metabolic syndrome (MetS), contributes to atherosclerotic and cardiometabolic disorders. Due to days-long analysis, current clinical procedures for cardiotoxic blood lipid monitoring are unmet. This study used AI-assisted attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy to identify MetS and precisely quantify multiple blood lipid levels with a blood sample of 0.

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Genetic influence on choroidal vascularity index.

PLoS One

January 2025

Department of Ophthalmology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Purpose: To investigate the heritability of genetic influence on macular choroidal vascularity index (CVI).

Methods: Total choroidal area (TCA), luminal area (LA), and CVI was measured using horizontal scan of spectral-domain optical coherence tomography with enhanced depth imaging in the 373 healthy twin participants. Characteristics of the participants were investigated, including age, sex, axial length, hypertension, diabetes, drinking habits, and smoking status.

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Purpose: Individuals with Down syndrome (DS) have reduced visual acuity (VA), even when wearing refractive correction. The relationship between refractive error and VA in adults with DS is explored.

Methods: Thirty adults with DS (age = 29 ± 10 years) were enrolled in a trial comparing clinical and objectively determined refractions.

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Uncorrected refractive error is a key cause of childhood visual impairment in the United States. As pediatric vision issues are often asymptomatic, vision screenings are essential to identify children's eye problems. Despite the importance of vision for children's health, well-being, and academic achievement, challenges remain in ensuring that children have equitable access to vision screenings and follow-up eye care.

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Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification.

Heliyon

January 2025

Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Kurdistan Regain, Iraq.

Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions.

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