Association of Retinal Age Gap and Risk of Kidney Failure: A UK Biobank Study

Zhang, Shiran; Chen, Ruiye; Wang, Yan; Hu, Wenyi; Kiburg, Katerina; Zhang, Junyao; Yang, Xiaohong; Yu, Honghua; Wang, Wei; Wang, Wei; Zhu, Zhuoting

doi:10.1053/j.ajkd.2022.09.018

Cited by 14 publications

(14 citation statements)

References 54 publications

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“…Moreover, this approach is particularly effective with small data sets, such as OCT and OCTA, where it can achieve strong performance. Besides, it is worth noting that certain DL methods can also be leveraged to extract retinal characteristic biomarkers to aid in disease diagnosis and prediction 46–49 …”

Section: Two Research Patterns For Analyzing Ocular Datamentioning

confidence: 99%

“…The associations between the retinal age gap and the risk of kidney disease were subsequently evaluated. The findings indicated that there was a significant association between the retinal age gap and the risk of developing kidney failure, with each additional year of retinal age gap contributing to a 10% increase in the risk of incident kidney failure independently (HR = 1.10) 46 . While the retinal age gap biomarker has demonstrated excellent performance in identifying various systemic diseases, the absence of specific analysis and differentiation between these diseases may limit its practical application in real clinical settings.…”

Section: Application Of Ai Using Ocular Images For Systemic Diseases ...mentioning

confidence: 99%

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Advancing systemic disease diagnosis through ophthalmic image‐based artificial intelligence

Miao,

Zou,

et al. 2024

MedComm – Future Medicine

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

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Section: Two Research Patterns For Analyzing Ocular Datamentioning

confidence: 99%

Section: Application Of Ai Using Ocular Images For Systemic Diseases ...mentioning

confidence: 99%

Advancing systemic disease diagnosis through ophthalmic image‐based artificial intelligence

Miao,

Zou,

et al. 2024

MedComm – Future Medicine

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“…Longitudinal studies showed the retinal age gap independently predicted the risk for all-cause and cause-specific mortality 208,212 and age-related morbidities, including neurodegenerative disease, 215 cardiovascular diseases, 208,216 and kidney failure. 217…”

Section: Retinal Agementioning

confidence: 99%

Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

Chen

Wang

Zhang

et al. 2023

MedComm – Future Medicine

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Given the unprecedented phenomenon of population ageing, studies have increasing captured the heterogeneity within the ageing process. In this context, the concept of "biological age" has been introduced as an integrated measure reflecting the individualized ageing pace. Identifying reliable and robust biomarkers of age is critical for the accurate risk stratification of individuals and exploration into antiageing interventions. Numerous potential biomarkers of ageing have been proposed, spanning from molecular changes and imaging characteristics to clinical phenotypes. In this review, we will start off with a discussion of the development of ageing biomarkers, then we will provide a comprehensive summary of currently identified ageing biomarkers in humans, discuss the rationale behind each biomarker and highlight their accuracy and clinical value with a contemporary perspective. Additionally, we MedComm -Future Medicine.

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“…And the incidence of CKD or advanced CKD in high‐risk groups was significantly different from medium‐risk groups, while no differences were observed in low‐risk groups of CKD ( p = 0.212 and 0.689 in the internal and external longitudinal test sets, respectively). A DL model was developed by Zhang et al 100 . to characterize retinal age from retinal images and the retinal age gap (difference between model‐based retinal age and chronological age) was used to predict the risk of ESRD.…”

Section: Guiding Treatment and Predicting Prognosis From Retinal Imagingmentioning

confidence: 99%

“…And the incidence of CKD or advanced CKD in high-risk groups was significantly different from medium-risk groups, while no differences were observed in low-risk groups of CKD (p = 0.212 and 0.689 in the internal and external longitudinal test sets, respectively). A DL model was developed by Zhang et al 100 increase in the risk of incident ESRD with each one-year increase in retinal age gap (HR = 1.10, 95% CI: 1.03-1.17).…”

Section: Predicting the Prognosis And Mortality Of Ckdmentioning

confidence: 99%

Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives

Wen

Liu

et al. 2023

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Artificial intelligence (AI) has reformed the healthcare system with its compelling capabilities of processing biomedical data for disease diagnosis, prediction, and individualized management. The eye, as a non-invasive observation window for many systemic diseases, can be used to detect the signs of chronic kidney diseases, and other diseases like hypertension and type 2 diabetes mellitus, based on specific manifestations of retinal images. Recent advances using AI technology have posed a great potential of using retinal images for rapid mass screening and prognosis prediction of kidney diseases. Herein, we outlined the key applications of AI in ophthalmology and the detection of systemic diseases based on retinal imaging, especially the current progress of retinal image-based AI models for the detection and prediction of kidney diseases. We hope to shed light on the current opportunities and future challenges in this field to provide suggestions for further improvement and applications.

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Association of Retinal Age Gap and Risk of Kidney Failure: A UK Biobank Study

Cited by 14 publications

References 54 publications

Advancing systemic disease diagnosis through ophthalmic image‐based artificial intelligence

Advancing systemic disease diagnosis through ophthalmic image‐based artificial intelligence

Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives

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