Population-based Normative Reference for Retinal Microvascular Atlas DOI
Mayinuer Yusufu, Algis J. Vingrys, Xianwen Shang

et al.

Published: Oct. 27, 2024

Abstract Objective To establish the normative range of a comprehensive set retinal vascular measurements to better understand their value as biomarkers for assessing ocular and systemic health. Methods This cross-sectional study used data from UK Biobank. Retina-based Microvascular Health Assessment System (RMHAS) software was extract measurements, including Calibre, Complexity, Density, Branching Angle, Tortuosity, differentiating between arteries veins, macula periphery. In addition, we explored relationships those health metrics, age, systolic blood pressure (SBP), body mass index, glycated hemoglobin, intraocular pressure. Results Among 10,151 healthy participants, reported 114 stratified by sex age. The mean values Central Retinal Artery Equivalent (CRAE) Vein (CRVE) were 152 (standard deviation=14.9) μm 233 (21.5) respectively. Fractal Dimension (FD) 1.77 (0.032), with arterial FD 1.53 (0.039) venular 1.56 (0.025). Age SBP showed strongest associations most parameters among metrics. CRAE, CRVE, Complexity decreased increasing age SBP. Changes in generally greater than venous measurements. Generalized Additive Models further revealed that observed mainly linear. Conclusions By establishing population our enables quantifiable approaches changes.

Language: Английский

Retinal Imaging-Based Oculomics: Artificial Intelligence as a Tool in the Diagnosis of Cardiovascular and Metabolic Diseases DOI Creative Commons
Laura Andreea Ghenciu,

Mirabela Dima,

Emil Robert Stoicescu

et al.

Biomedicines, Journal Year: 2024, Volume and Issue: 12(9), P. 2150 - 2150

Published: Sept. 23, 2024

Cardiovascular diseases (CVDs) are a major cause of mortality globally, emphasizing the need for early detection and effective risk assessment to improve patient outcomes. Advances in oculomics, which utilize relationship between retinal microvascular changes systemic vascular health, offer promising non-invasive approach assessing CVD risk. Retinal fundus imaging optical coherence tomography/angiography (OCT/OCTA) provides critical information diagnosis, with parameters such as vessel caliber, tortuosity, branching patterns identified key biomarkers. Given large volume data generated during routine eye exams, there is growing automated tools aid diagnosis prediction. The study demonstrates that AI-driven analysis images can accurately predict cardiovascular factors, events, metabolic diseases, surpassing traditional diagnostic methods some cases. These models achieved area under curve (AUC) values ranging from 0.71 0.87, sensitivity 71% 89%, specificity 40% 70%, This highlights potential component personalized medicine, enabling more precise earlier intervention. It not only aids detecting abnormalities may precede events but also offers scalable, non-invasive, cost-effective solution widespread screening. However, article emphasizes further research standardize protocols validate clinical utility these biomarkers across different populations. By integrating oculomics into practice, healthcare providers could significantly enhance management ultimately improving Fundus image thus represents valuable tool future precision medicine health management.

Language: Английский

Citations

9
Oculomics: Current Concepts and Evidence DOI Creative Commons
Zhuoting Zhu, Yueye Wang, Ziyi Qi

et al.

Progress in Retinal and Eye Research, Journal Year: 2025, Volume and Issue: unknown, P. 101350 - 101350

Published: March 1, 2025

Language: Английский

Citations

1
Retinal Vascular Measurements and Mortality Risk: Evidence From the UK Biobank Study DOI Creative Commons
Mayinuer Yusufu, Yutong Chen, Alimu Dayimu

et al.

Translational Vision Science & Technology, Journal Year: 2024, Volume and Issue: 13(1), P. 2 - 2

Published: Jan. 2, 2024

Purpose: This study aimed to investigate the association between quantitative retinal vascular measurements and risk of all-cause premature mortality. Methods: In this population-based cohort using UK Biobank data, we employed Retina-based Microvascular Health Assessment System assess fundus images for image quality extracted 392 per image. These encompass six categories features: caliber, density, length, tortuosity, branching angle, complexity. Univariate Cox regression models were used identify potential indicators mortality data on from death registries. Multivariate then test these associations while controlling confounding factors. Results: The final analysis included 66,415 participants. After adjusting demographic, health, lifestyle factors genetic score, 18 10 significantly associated with mortality, respectively. fully adjusted model, following different features mortality: arterial bifurcation density (branching angle), number segments (complexity), interquartile range median absolute deviation curve angle (tortuosity), mean values pixel widths all in each (caliber), skeleton arteries macular area (density), minimum venular arc length (length). Conclusions: revealed Those identified parameters should be further studied biological mechanisms connecting them increased risk. Translational Relevance: identifies biomarkers provides novel targets investigating underlying mechanisms.

Language: Английский

Citations

5
Oculomics: A Crusade Against the Four Horsemen of Chronic Disease DOI Creative Commons
Emily J Patterson, Alistair D. Bounds, Siegfried Wagner

et al.

Ophthalmology and Therapy, Journal Year: 2024, Volume and Issue: 13(6), P. 1427 - 1451

Published: April 17, 2024

Chronic, non-communicable diseases present a major barrier to living long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, treatment efforts, improving patient outcomes. There is therefore critical need make screening techniques as accessible, unintimidating, cost-effective possible. The association between ocular biomarkers systemic health disease (oculomics) presents an attractive opportunity for detection of diseases, ophthalmic are often relatively low-cost, fast, non-invasive. this review, we highlight the key associations structural in eye four globally leading causes morbidity mortality: cardiovascular disease, cancer, neurodegenerative metabolic disease. We observe that particularly promising target oculomics, with detected multiple structures. Cardiovascular choroid, retinal vasculature, nerve fiber layer, eyelid, tear fluid, lens, vasculature. contrast, only fluid emerged cancer. retina rich source oculomics data, analysis which has been enhanced by artificial intelligence-based tools. Although not all disease-specific, limiting their current diagnostic utility, future research will likely benefit from combining data various structures improve specificity, well active design, development, optimization instruments specific signatures, thus facilitating differential diagnoses. Long-term stop people lives. help prevent, monitor, treat patients' health. order diagnose tools easy patients access, painless, low-cost. may provide solution. discuss link changes types long-term that, together, kill most population: (1) (affecting heart and/or blood). (2) Cancer (abnormal growth cells). (3) Neurodegenerative brain nervous system). (4) Metabolic (problems storing, accessing, using body's fuel). show leaves tell-tale signs lots different parts eye. Signs mostly found back eye, cancer be fluid. seen them tell us what is. believe understand more about how detect it if combine information within develop new these

Language: Английский

Citations

5
Application of ChatGPT-4 to oculomics: a cost-effective osteoporosis risk assessment to enhance management as a proof-of-principles model in 3PM DOI
Joon Yul Choi,

Eoksoo Han,

Tae Keun Yoo

et al.

The EPMA Journal, Journal Year: 2024, Volume and Issue: 15(4), P. 659 - 676

Published: Aug. 28, 2024

Language: Английский

Citations

5
Population-based Normative Reference for Retinal Microvascular Atlas DOI Creative Commons
Mayinuer Yusufu, Algis J. Vingrys, Xianwen Shang

et al.

Ophthalmology Science, Journal Year: 2025, Volume and Issue: 5(3), P. 100723 - 100723

Published: Feb. 1, 2025

Language: Английский

Citations

0
Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES DOI Creative Commons
Yuqi Zhang, Sijin Li, Weijie Wu

et al.

BioData Mining, Journal Year: 2024, Volume and Issue: 17(1)

Published: April 22, 2024

Abstract Background Recent researches have found a strong correlation between the triglyceride-glucose (TyG) index or atherogenic of plasma (AIP) and cardiovascular disease (CVD) risk. However, there is lack research on non-invasive rapid prediction We aimed to develop validate machine-learning model for predicting risk based variables encompassing clinical questionnaires oculomics. Methods collected data from Korean National Health Nutrition Examination Survey (KNHANES). The training dataset (80% year 2008 2011 KNHANES) was used machine learning development, with internal validation using remaining 20%. An external 2012 assessed model’s predictive capacity TyG-index AIP in new cases. included 32122 participants final dataset. Machine models 25 algorithms were trained oculomics measurements predict range AIP. area under receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score evaluate performance our models. Results Based large-scale cohort studies, we determined cut-off points at 8.0, 8.75 (upper one-third values), 8.93 one-fourth cut-offs 0.318, 0.34. Values surpassing these thresholds indicated elevated best-performing algorithm revealed 8.75, AUCs 0.812, 0.873, 0.911, respectively. External 0.809, 0.863, 0.901. For 0.34, achieved similar 0.849 0.842. Slightly lower seen 0.318 cut-off, 0.844 0.836. Significant gender-based variations noted 8 (male AUC=0.832, female AUC=0.790) AUC=0.874, AUC=0.862) AUC=0.853, AUC=0.825) 0.34 AUC=0.858, AUC=0.831). Gender similarity AUC AUC=0.907 versus AUC=0.906) observed only when point equals 8.93. Conclusion established simple effective that has good value general population.

Language: Английский

Citations

3
Advancing systemic disease diagnosis through ophthalmic image‐based artificial intelligence DOI Creative Commons
Hanpei Miao,

Zixing Zou,

Jie Xu

et al.

MedComm – Future Medicine, Journal Year: 2024, Volume and Issue: 3(1)

Published: March 1, 2024

Abstract The eye serves as a unique window into systemic health, offering clinicians valuable opportunity for early detection and targeted treatment. Against this backdrop, advancements in artificial intelligence (AI) ophthalmic imaging are converging to pave the way more precise predictive diagnostics. This review aims elucidate transformative role of AI utilizing prediction diseases. We begin by introducing advantages tool detecting also provide an overview various techniques that have proven useful predicting ailments. Then, we summarize two research patterns analyzing ocular data, followed introduction current applications using images significantly increase diagnostic precision. Despite promise, challenges such data heterogeneity model interpretability persist, which covered review. conclude discussing future directions immense potential these AI‐enabled approaches hold revolutionizing healthcare. As technologies advance, their integration with offers promising avenues improving diagnosis, prediction, management diseases, thereby contributing evolving landscape integrated

Language: Английский

Citations

2
Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies DOI
Fan Yang,

Wendusubilige,

Jingwei Kong

et al.

The EPMA Journal, Journal Year: 2023, Volume and Issue: 14(3), P. 417 - 442

Published: July 31, 2023

Language: Английский

Citations

5
Cross-modality Labeling Enables Noninvasive Capillary Quantification as a Sensitive Biomarker for Assessing Cardiovascular Risk DOI Creative Commons
Danli Shi, Yukun Zhou,

Shuang He

et al.

Ophthalmology Science, Journal Year: 2023, Volume and Issue: 4(3), P. 100441 - 100441

Published: Dec. 5, 2023

We aim to use fundus fluorescein angiography (FFA) label the capillaries on color (CF) photographs and train a deep learning model quantify retinal noninvasively from CF apply it cardiovascular disease (CVD) risk assessment.

Language: Английский

Citations

2