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Amir Hamzah NA, Wan Zaki WMD, Wan Abdul Halim WH, Mustafar R, Saad AH. Evaluating the potential of retinal photography in chronic kidney disease detection: a review. PeerJ 2024; 12:e17786. [PMID: 39104365 PMCID: PMC11299532 DOI: 10.7717/peerj.17786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/30/2024] [Indexed: 08/07/2024] Open
Abstract
Background Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. Objectives The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. Survey Methodology A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Results Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. Conclusion In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.
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Affiliation(s)
- Nur Asyiqin Amir Hamzah
- Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia
- Faculty of Engineering and Technology, Multimedia University, Ayer Keroh, Melaka, Malaysia
| | - Wan Mimi Diyana Wan Zaki
- Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia
| | | | - Ruslinda Mustafar
- Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Kuala Lumpur, Malaysia
| | - Assyareefah Hudaibah Saad
- Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia
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Liu F, Chen X, Wang Q, Lin W, Li Y, Zhang R, Huang H, Jiang S, Niu Y, Liu W, Wang L, Zhang W, Zheng Y, Cao X, Wang Y, Wu J, Zhang L, Tang L, Zhou J, Chen P, Cai G, Dong Z. Correlation between retinal vascular geometric parameters and pathologically diagnosed type 2 diabetic nephropathy. Clin Kidney J 2024; 17:sfae204. [PMID: 39099565 PMCID: PMC11292218 DOI: 10.1093/ckj/sfae204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Indexed: 08/06/2024] Open
Abstract
Background Diabetic nephropathy (DN) and diabetic retinopathy (DR) are common microvascular complications of diabetes. The purpose of this study was to investigate the correlation between retinal vascular geometric parameters and pathologically diagnosed type 2 DN and to determine the capacity of retinal vascular geometric parameters in differentiating DN from non-diabetic renal disease (NDRD). Methods The study participants were adult patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease who underwent a renal biopsy. Univariate and multivariable regression analyses were performed to evaluate associations between retinal vessel geometry parameters and pathologically diagnosed DN. Multivariate binary logistic regression analyses were performed to establish a differential diagnostic model for DN. Results In total, 403 patients were examined in this cross-sectional study, including 152 (37.7%) with DN, 157 (39.0%) with NDRD and 94 (23.3%) with DN combined with NDRD. After univariate logistic regression, total vessel fractal dimension, arteriolar fractal dimension and venular fractal dimension were all found to be associated with DN. In multivariate analyses adjusting for age, sex, blood pressure, diabetes, DR and other factors, smaller retinal vascular fractal dimensions were significantly associated with DN (P < .05). We developed a differential diagnostic model for DN combining traditional clinical indicators and retinal vascular geometric parameters. The area under the curve of the model established by multivariate logistic regression was 0.930. Conclusions Retinal vessel fractal dimension is of great significance for the rapid and non-invasive differentiation of DN. Incorporating retinal vessel fractal dimension into the diagnostic model for DN and NDRD can improve the diagnostic efficiency.
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Affiliation(s)
- Fang Liu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
- First Clinical Medical College, Guangdong Pharmaceutical University, Guangzhou, China
| | - Xiaoniao Chen
- Senior Department of Ophthalmology, Third Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qian Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Wenwen Lin
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
- First Clinical Medical College, Guangdong Pharmaceutical University, Guangzhou, China
| | - Ying Li
- Senior Department of Ophthalmology, Third Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ruimin Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
- College of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hui Huang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
- First Clinical Medical College, Guangdong Pharmaceutical University, Guangzhou, China
| | - Shuangshuang Jiang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Yue Niu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Weicen Liu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Liqiang Wang
- Senior Department of Ophthalmology, Third Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Weiguang Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Ying Zheng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Xueying Cao
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Yong Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Jie Wu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Li Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Li Tang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Jianhui Zhou
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Pu Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Guangyan Cai
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Zheyi Dong
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
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Li R, Hui Y, Zhang X, Zhang S, Lv B, Ni Y, Li X, Liang X, Yang L, Lv H, Yin Z, Li H, Yang Y, Liu G, Li J, Xie G, Wu S, Wang Z. Ocular biomarkers of cognitive decline based on deep-learning retinal vessel segmentation. BMC Geriatr 2024; 24:28. [PMID: 38184539 PMCID: PMC10770952 DOI: 10.1186/s12877-023-04593-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 12/13/2023] [Indexed: 01/08/2024] Open
Abstract
BACKGROUND The current literature shows a strong relationship between retinal neuronal and vascular alterations in dementia. The purpose of the study was to use NFN+ deep learning models to analyze retinal vessel characteristics for cognitive impairment (CI) recognition. METHODS We included 908 participants from a community-based cohort followed for over 15 years (the prospective KaiLuan Study) who underwent brain magnetic resonance imaging (MRI) and fundus photography between 2021 and 2022. The cohort consisted of both cognitively healthy individuals (N = 417) and those with cognitive impairment (N = 491). We employed the NFN+ deep learning framework for retinal vessel segmentation and measurement. Associations between Retinal microvascular parameters (RMPs: central retinal arteriolar / venular equivalents, arteriole to venular ratio, fractal dimension) and CI were assessed by Pearson correlation. P < 0.05 was considered statistically significant. The correlation between the CI and RMPs were explored, then the correlation coefficients between CI and RMPs were analyzed. Random Forest nonlinear classification model was used to predict whether one having cognitive decline or not. The assessment criterion was the AUC value derived from the working characteristic curve. RESULTS The fractal dimension (FD) and global vein width were significantly correlated with the CI (P < 0.05). Age (0.193), BMI (0.154), global vein width (0.106), retinal vessel FD (0.099), and CRAE (0.098) were the variables in this model that were ranked in order of feature importance. The AUC values of the model were 0.799. CONCLUSIONS Establishment of a predictive model based on the extraction of vascular features from fundus images has a high recognizability and predictive power for cognitive function and can be used as a screening method for CI.
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Affiliation(s)
- Rui Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ying Hui
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | | | - Shun Zhang
- Department of Psychiatry, Kailuan Mental Health Centre, Hebei province, Tangshan, China
| | - Bin Lv
- Ping An Healthcare Technology, Beijing, China
| | - Yuan Ni
- Ping An Healthcare Technology, Beijing, China
| | - Xiaoshuai Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xiaoliang Liang
- Department of Psychiatry, Kailuan Mental Health Centre, Hebei province, Tangshan, China
| | - Ling Yang
- School of Public Health, North China University of Science and Technology, Hebei province, Tangshan, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhiyu Yin
- Longzhen Senior Care, Beijing, China
| | - Hongyang Li
- Department of Ophthalmology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yingping Yang
- Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guangfeng Liu
- Department of Ophthalmology, Peking University International Hospital, Beijing, China
| | - Jing Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
| | - Guotong Xie
- Ping An Healthcare Technology, Beijing, China.
| | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, 57 Xinhua E Rd, Tangshan, China.
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
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Li R, Hui Y, Li J, Zhang X, Zhang S, Lv B, Ni Y, Li X, Liang X, Yang L, Lv H, Li H, Yang Y, Liu G, Xie G, Wu S, Wang Z. The association of global vessel width with cognitive decline and cerebral small vessel disease burden in the KaiLuan study. Quant Imaging Med Surg 2024; 14:932-943. [PMID: 38223087 PMCID: PMC10784051 DOI: 10.21037/qims-23-927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 11/10/2023] [Indexed: 01/16/2024]
Abstract
Background As the retinal microvasculature shares similarities with the cerebral microvasculature, numerous studies have shown that retinal vascular is associated with cognitive decline. In addition, several population-based studies have confirmed the association between retinal vascular and cerebral small vessel disease (CSVD) burden. However, the association of retinal vascular with CSVD burden as well as cognitive function has not been explored simultaneously. This study investigated the relations of retinal microvascular parameters (RMPs) with CSVD burden and cognitive function. Methods We conducted a cross-sectional study of participants in the KaiLuan study. Data were collected from subjects aged ≥18 years old who could complete retinal photography and brain magnetic resonance imaging (MRI) between December 2020 to October 2021 in the Kailuan community of Tangshan. RMPs were evaluated using a deep learning system. The cognitive function was measured using the Montreal Cognitive Assessment (MoCA). We conducted logistic regression models, and mediation analysis to evaluate the associations of RMPs with CSVD burden and cognitive decline. Results Of the 905 subjects (mean age: 55.42±12.02 years, 54.5% female), 488 (53.9%) were classified with cognitive decline. The fractal dimension (FD) [odds ratio (OR), 0.098, 95% confidence interval (CI): 0.015-0.639, P=0.015] and global vein width (OR: 1.010, 95% CI: 1.005-1.015, P<0.001) were independent risk factors for cognitive decline after adjustment for potential confounding factors. The global artery width was significantly associated with severe CSVD burden (OR: 0.985, 95% CI: 0.974-0.997, P=0.013). The global vein width was sightly associated with severe CSVD burden (OR: 1.005, 95% CI: 1.000-1.010, P=0.050) after adjusting for potential confounders. The multivariable-adjusted odds ratios (95% CI) in highest tertile versus lowest tertile of global vein width were 1.290 (0.901-1.847) for cognitive decline and 1.546 (1.004-2.290) for severe CSVD burden, respectively. Moreover, CSVD burden played a partial mediating role in the association between global vein width and cognitive function (mediating effect 6.59%). Conclusions RMPs are associated with cognitive decline and the development of CSVD. A proportion of the association between global vein width and cognitive decline may be attributed to the presence of CSVD burden.
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Affiliation(s)
- Rui Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ying Hui
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jing Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | | | - Shun Zhang
- Department of Psychiatry, Kailuan Mental Health Centre, Tangshan, China
| | - Bin Lv
- Ping An Healthcare Technology, Beijing, China
| | - Yuan Ni
- Ping An Healthcare Technology, Beijing, China
| | - Xiaoshuai Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xiaoliang Liang
- Department of Psychiatry, Kailuan Mental Health Centre, Tangshan, China
| | - Ling Yang
- School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Hongyang Li
- Department of Ophthalmology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yingping Yang
- Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guangfeng Liu
- Department of Ophthalmology, Peking University International Hospital, Beijing, China
| | - Guotong Xie
- Ping An Healthcare Technology, Beijing, China
| | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, Tangshan, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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Chikumba S, Hu Y, Luo J. Deep learning-based fundus image analysis for cardiovascular disease: a review. Ther Adv Chronic Dis 2023; 14:20406223231209895. [PMID: 38028950 PMCID: PMC10657535 DOI: 10.1177/20406223231209895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
It is well established that the retina provides insights beyond the eye. Through observation of retinal microvascular changes, studies have shown that the retina contains information related to cardiovascular disease. Despite the tremendous efforts toward reducing the effects of cardiovascular diseases, they remain a global challenge and a significant public health concern. Conventionally, predicting the risk of cardiovascular disease involves the assessment of preclinical features, risk factors, or biomarkers. However, they are associated with cost implications, and tests to acquire predictive parameters are invasive. Artificial intelligence systems, particularly deep learning (DL) methods applied to fundus images have been generating significant interest as an adjunct assessment tool with the potential of enhancing efforts to prevent cardiovascular disease mortality. Risk factors such as age, gender, smoking status, hypertension, and diabetes can be predicted from fundus images using DL applications with comparable performance to human beings. A clinical change to incorporate DL systems for the analysis of fundus images as an equally good test over more expensive and invasive procedures may require conducting prospective clinical trials to mitigate all the possible ethical challenges and medicolegal implications. This review presents current evidence regarding the use of DL applications on fundus images to predict cardiovascular disease.
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Affiliation(s)
- Symon Chikumba
- Department of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Department of Optometry, Faculty of Healthy Sciences, Mzuzu University, Luwinga, Mzuzu, Malawi
| | - Yuqian Hu
- Department of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jing Luo
- Department of Ophthalmology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin RD, Changsha, Hunan, China
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Wang X, Yao J, Li S, Zhang W, Wang L, Zhou A. Panretinal photocoagulation plus intravitreal conbercept for diabetic retinopathy in real world: a retrospective study. BMC Ophthalmol 2023; 23:400. [PMID: 37794367 PMCID: PMC10552403 DOI: 10.1186/s12886-023-03157-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 09/28/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND To evaluate outcomes of panretinal photocoagulation (PRP) plus intravitreal conbercept (IVC) for diabetic retinopathy (DR) in real world and explore risk factors for patients with poor reactivity and presence of vision-threatening complications after combination treatment. METHODS Retrospective review of DR patients received PRP plus IVC over 6 months. The main outcome was improvement ≥ 2 steps in ETDRS diabetic retinopathy severity scale (DRSS) levels. Different strategies for eyes receiving IVC within or over 1 month after PRP were analyzed. For patients with DRSS improvement < 2 steps and presence of vision-threatening adverse events, a binary logistic regression method was used to select risk factors. RESULTS Sixty one eyes were involved in this study. After treated with combination therapy with a median number of 3 injections, 44% of eyes improved ≥ 2 steps in DRSS levels. A total of 14 eyes (23%) occurred vision-threatening adverse events. No significant difference was found in eyes receiving conbercept within or over 1 month after PRP. Duration of diabetes (OR 0.849, 95%CI 0.734-0.982, P = 0.027), GFR (OR 0.961, 95%CI 0.933-0.990, P = 0.010) and baseline DRSS levels (OR 3.290, 95%CI 1.483-7.295, P = 0.003) were independent risk factors for DRSS improvement < 2 steps after treatment. Occurrence of vision-threatening complications was only related to high DRSS levels (OR 3.668, 95%CI 1.710-7.868, P = 0.001). CONCLUSIONS The combination therapy was effective for most patients with DR in real world. Eyes received PRP combined with earlier or later conbercept was demonstrated no significant difference for outcomes. For patients with poor renal function, high DRSS levels or occurred DR at the early stage of diabetes, follow-up should be strengthened.
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Affiliation(s)
- Xin Wang
- Department of Ophthalmology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China
| | - Jing Yao
- Department of Ophthalmology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China
| | - Shengen Li
- Department of Ophthalmology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China
| | - Wenyi Zhang
- Department of Ophthalmology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China
| | - Lijun Wang
- Department of Ophthalmology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China
| | - Aiyi Zhou
- Department of Ophthalmology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China.
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Correlation of Glucose and Lipid Metabolism Levels and Serum Uric Acid Levels with Diabetic Retinopathy in Type 2 Diabetic Mellitus Patients. Emerg Med Int 2022; 2022:9201566. [PMID: 35912387 PMCID: PMC9334055 DOI: 10.1155/2022/9201566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/11/2022] [Indexed: 11/17/2022] Open
Abstract
Objective. The aim of this study was to observe the association between the development of diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) and the levels of glucose and lipid metabolism and serum uric acid (SUA) levels. Methods. A retrospective analysis was performed on 97 patients with T2DM who were admitted to our endocrinology department from June 2019 to April 2021 with complete data; the patients were divided into DR and no DR groups (NDR) according to the presence or absence of DR. Their clinical history and biochemical test indexes were collected, and the fundus was examined by fundus photography and the fundoscopic examination method, and the vascular diameter was measured by using a computer software. All clinical data, medical history, and biochemical test indexes were compared between the two groups, and logistic regression was used to analyze the risk factors of DR. Results. The duration of DM disease, fasting blood glucose (FBG), glycosylated hemoglobin, type A1C (HbA1c) levels, cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C), and SUA levels were higher in the DR group than those in the NDR group, and the differences were significant (
). The difference between the NDR group and the DR group in terms of gender, age, BMI, DBP, SBP, family history of DM, FINS, and HDL-C levels was not significant (
). The results of multifactorial analysis showed that the four variables of DM duration, HbA1c, TG, and SUA were still risk factors for the development of DR (
). Further receiver operating characteristic (ROC) analysis showed that the areas under the curves (AUCs) for the duration of DM disease, HbA1c, TG, and SUA to predict the occurrence of DR were 0.740 (95% CI 0.639–0.841), 0.767 (95% 0.672–0.862), 0.721 (95% CI 0.617–0.826), and 0.693 (95% CI 0.588∼0.797), respectively. Conclusion. The lesions of DR in T2DM patients have a close relationship with the course of DM, HbA1c, TG, and SUA, and the course of DM, HbA1c, TG, and SUA has a good predictive value for the occurrence of DR.
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Effects of intravitreal bevacizumab therapy on glomerular filtration rate in patients treated for proliferative diabetic retinopathy. Retina 2022; 42:1926-1930. [DOI: 10.1097/iae.0000000000003562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Guo Y, Liu S, Xu H. Uric Acid and Diabetic Retinopathy: A Systematic Review and Meta-Analysis. Front Public Health 2022; 10:906760. [PMID: 35712295 PMCID: PMC9197488 DOI: 10.3389/fpubh.2022.906760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Background The relationship between uric acid (UA) and diabetic retinopathy (DR) remains ambiguous, and the results of current studies on the UA levels in patients with DR are conflicting. A meta-analysis was performed to provide a better understanding of the relationship between UA levels and DR. Methods PubMed, Web of Science, Embase, and the Cochrane Library databases were searched until December 11, 2021 to identify eligible studies, that compared the UA levels of the case group (patients with DR) and control group (controls with diabetes and healthy participants). The weighted mean difference (WMD) with a 95% confidence interval (CI) was used to evaluate the difference in UA levels between the case and control groups. Results Twenty-one studies involving 4,340 patients with DR and 8,595 controls (8,029 controls with diabetes and 566 healthy participants) were included in this meta-analysis. We found that patients with DR had significantly higher UA levels than those in the controls with diabetes (WMD = 36.28; 95% CI: 15.68, 56.89; P < 0.001) and healthy participants (WMD = 70.80; 95% CI: 19.85, 121.75; P = 0.006). There was an obvious heterogeneity among the 21 studies (I2 = 97%, P < 0.001). Subgroup analyses of different phases of DR showed that UA levels were significantly increased in participants with proliferative diabetic retinopathy (PDR) (WMD = 46.57; 95% CI: 28.51, 64.63; P < 0.001) than in controls with diabetes; however, the difference is not statistically significant when comparing UA levels in patients with non-proliferative diabetic retinopathy (NPDR) and controls with diabetes (WMD = 22.50; 95% CI: −6.07, 51.08; P = 0.120). In addition, UA levels were higher in participants with a body mass index (BMI) ≥25.0 kg/m2 and over 15 years of diabetes. Univariate meta-regression analysis revealed that BMI (P = 0.007, Adj R2 = 40.12%) and fasting blood glucose (FBG) (P = 0.040, Adj R2 = 29.72%) contributed to between-study heterogeneity. Conclusions In conclusion, our study provides evidence that UA levels are higher in patients with DR than those in the controls, but this difference is not statistically significant in the early phases. UA might be a potential biomarker for identifying disease severity in patients with DR, rather than predicting the onset of DR among patients with diabetes. However, more prospective and high-quality clinical evidence is required to confirm these present findings. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=297708.
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Affiliation(s)
- Yicong Guo
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, China
| | - Siyue Liu
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, China
| | - Huilan Xu
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, China
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Prediction of Diabetes through Retinal Images Using Deep Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7887908. [PMID: 35694596 PMCID: PMC9187442 DOI: 10.1155/2022/7887908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/19/2022] [Accepted: 05/17/2022] [Indexed: 12/15/2022]
Abstract
Microvascular problems of diabetes, such as diabetic retinopathy and macular edema, can be seen in the eye's retina, and the retinal images are being used to screen for and diagnose the illness manually. Using deep learning to automate this time-consuming process might be quite beneficial. In this paper, a deep neural network, i.e., convolutional neural network, has been proposed for predicting diabetes through retinal images. Before applying the deep neural network, the dataset is preprocessed and normalised for classification. Deep neural network is constructed by using 7 layers, 5 kernels, and ReLU activation function, and MaxPooling is implemented to combine important features. Finally, the model is implemented to classify whether the retinal image belongs to a diabetic or nondiabetic class. The parameters used for evaluating the model are accuracy, precision, recall, and F1 score. The implemented model has achieved a training accuracy of more than 95%, which is much better than the other states of the art algorithms.
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Zhao X, Meng L, Su H, Lv B, Lv C, Xie G, Chen Y. Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images. Front Cell Dev Biol 2022; 10:888268. [PMID: 35663399 PMCID: PMC9160874 DOI: 10.3389/fcell.2022.888268] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 04/07/2022] [Indexed: 11/30/2022] Open
Abstract
Background: Anemia is the most common hematological disorder. The purpose of this study was to establish and validate a deep-learning model to predict Hgb concentrations and screen anemia using ultra-wide-field (UWF) fundus images. Methods: The study was conducted at Peking Union Medical College Hospital. Optos color images taken between January 2017 and June 2021 were screened for building the dataset. ASModel_UWF using UWF images was developed. Mean absolute error (MAE) and area under the receiver operating characteristics curve (AUC) were used to evaluate its performance. Saliency maps were generated to make the visual explanation of the model. Results: ASModel_UWF acquired the MAE of the prediction task of 0.83 g/dl (95%CI: 0.81–0.85 g/dl) and the AUC of the screening task of 0.93 (95%CI: 0.92–0.95). Compared with other screening approaches, it achieved the best performance of AUC and sensitivity when the test dataset size was larger than 1000. The model tended to focus on the area around the optic disc, retinal vessels, and some regions located at the peripheral area of the retina, which were undetected by non-UWF imaging. Conclusion: The deep-learning model ASModel_UWF could both predict Hgb concentration and screen anemia in a non-invasive and accurate way with high efficiency.
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Affiliation(s)
- Xinyu Zhao
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Lihui Meng
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Su
- Ping An Healthcare Technology Company Limited, Shenzhen, China
| | - Bin Lv
- Ping An Healthcare Technology Company Limited, Shenzhen, China
| | - Chuanfeng Lv
- Ping An Healthcare Technology Company Limited, Shenzhen, China
| | - Guotong Xie
- Ping An Healthcare Technology Company Limited, Shenzhen, China
- Ping An Health Cloud Company Limited, Shenzhen, China
- Ping An International Smart City Technology Company Limited, Shenzhen, China
- *Correspondence: Guotong Xie, ; Youxin Chen,
| | - Youxin Chen
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing, China
- *Correspondence: Guotong Xie, ; Youxin Chen,
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