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Lin W, Wang P, Qi Y, Zhao Y, Wei X. Progress and challenges of in vivo flow cytometry and its applications in circulating cells of eyes. Cytometry A 2024; 105:437-445. [PMID: 38549391 DOI: 10.1002/cyto.a.24837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 02/05/2024] [Accepted: 03/15/2024] [Indexed: 06/15/2024]
Abstract
Circulating inflammatory cells in eyes have emerged as early indicators of numerous major diseases, yet the monitoring of these cells remains an underdeveloped field. In vivo flow cytometry (IVFC), a noninvasive technique, offers the promise of real-time, dynamic quantification of circulating cells. However, IVFC has not seen extensive applications in the detection of circulating cells in eyes, possibly due to the eye's unique physiological structure and fundus imaging limitations. This study reviews the current research progress in retinal flow cytometry and other fundus examination techniques, such as adaptive optics, ultra-widefield retinal imaging, multispectral imaging, and optical coherence tomography, to propose novel ideas for circulating cell monitoring.
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Affiliation(s)
- Wei Lin
- Department of Public Scientific Research Platform, School of Clinical and Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
- Institute of Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Peng Wang
- Department of Public Scientific Research Platform, School of Clinical and Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
- Institute of Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Yingxin Qi
- Department of Public Scientific Research Platform, School of Clinical and Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
- Institute of Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Yanlong Zhao
- Department of Public Scientific Research Platform, School of Clinical and Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
- Institute of Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Xunbin Wei
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
- Biomedical Engineering Department, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- International Cancer Institute, Peking University, Beijing, China
- Department of Critical-care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
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2
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Tan Y, Ma Y, Rao S, Sun X. Performance of deep learning for detection of chronic kidney disease from retinal fundus photographs: A systematic review and meta-analysis. Eur J Ophthalmol 2024; 34:502-509. [PMID: 37671422 DOI: 10.1177/11206721231199848] [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] [Indexed: 09/07/2023]
Abstract
OBJECTIVE Deep learning has been used to detect chronic kidney disease (CKD) from retinal fundus photographs. We aim to evaluate the performance of deep learning for CKD detection. METHODS The original studies in CKD patients detected by deep learning from retinal fundus photographs were eligible for inclusion. PubMed, Embase, the Cochrane Library, and Web of Science were searched up to October 31, 2022. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the risk of bias. RESULTS Four studies enrolled 114,860 subjects were included. The pooled sensitivity and specificity were 87.8% (95% confidence interval (CI): 61.6% to 98.3%), and 62.4% (95% CI: 44.9% to 78.7%). The area under the curve (AUC) was 0.864 (95%CI: 0.769, 0.986). CONCLUSION Deep learning based on retinal fundus photographs has the ability to detect CKD, but it currently has a lot of room for improvement. It is still a long way from clinical application.
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Affiliation(s)
- Yuhe Tan
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yunxi Ma
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Suyun Rao
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xufang Sun
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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3
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Duan J, Liu D, Zhao Z, Liang L, Pan S, Tian F, Yu P, Li G, Liu Z. Short-term duration of diabetic retinopathy as a predictor for development of diabetic kidney disease. J Transl Int Med 2023; 11:449-458. [PMID: 38130638 PMCID: PMC10732346 DOI: 10.2478/jtim-2022-0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023] Open
Abstract
Background Diabetic retinopathy (DR) is a risk factor for diabetic kidney disease (DKD). Whether the duration, especially the short-term duration, of DR is associated with the development and progression of DKD remains unclear. Materials and Methods A retrospective study and two-sample Mendelian randomization (MR) analysis were conducted. Kidney disease was defined by the urinary albumin-to-creatinine ratio (ACR) and the estimated glomerular filtration rate (eGFR). DR was diagnosed by an expert ophthalmologist by using a digital fundus camera. Binary and ordinal logistic regression analyses were performed. A restricted cubic spline was utilized to detect nonlinear associations. Summary statistics for DR- and DKD-associated single-nuclear polymorphisms (SNPs) were extracted from the FinnGen and the UK Biobank consortia. Results A total of 2674 patients with type 2 diabetes mellitus (T2DM) and type 2 diabetic kidney disease (T2DKD) were included. The prevalence and mean duration of DR increased with elevation of ACR and decline in eGFR. Renal function was significantly reduced in patients with DR in the fifth year of life. Binary and ordinal logistic regression showed that each 1-year increase in DR duration was associated with a 19% risk increase in the development of DKD, 16% in the elevation of ACR, and 21% in the decline of renal function. MR estimates indicated that DR was causally associated with DKD development, with an odds ratio of 2.89. Conclusions DR and the duration of DR were independent risk factors for the development and progression of DKD. The short-term duration of DR may be associated with DKD development. DR had a statistically significant effect on DKD.
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Affiliation(s)
- Jiayu Duan
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou450052, Henan Province, China
- TCM-Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou450052, Henan Province, China
- Henan Province Research Center for Kidney Disease, Zhengzhou450052, Henan Province, China
| | - Dongwei Liu
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou450052, Henan Province, China
- TCM-Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou450052, Henan Province, China
- Henan Province Research Center for Kidney Disease, Zhengzhou450052, Henan Province, China
| | - Zihao Zhao
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou450052, Henan Province, China
- Henan Province Research Center for Kidney Disease, Zhengzhou450052, Henan Province, China
| | - Lulu Liang
- TCM-Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou450052, Henan Province, China
| | - Shaokang Pan
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou450052, Henan Province, China
- TCM-Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou450052, Henan Province, China
| | - Fei Tian
- TCM-Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou450052, Henan Province, China
| | - Pei Yu
- Henan Province Research Center for Kidney Disease, Zhengzhou450052, Henan Province, China
| | - Guangpu Li
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou450052, Henan Province, China
- TCM-Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou450052, Henan Province, China
| | - Zhangsuo Liu
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou450052, Henan Province, China
- TCM-Integrated Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou450052, Henan Province, China
- Henan Province Research Center for Kidney Disease, Zhengzhou450052, Henan Province, China
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An S, Vaghefi E, Yang S, Xie L, Squirrell D. Examination of alternative eGFR definitions on the performance of deep learning models for detection of chronic kidney disease from fundus photographs. PLoS One 2023; 18:e0295073. [PMID: 38032977 PMCID: PMC10688656 DOI: 10.1371/journal.pone.0295073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
Deep learning (DL) models have shown promise in detecting chronic kidney disease (CKD) from fundus photographs. However, previous studies have utilized a serum creatinine-only estimated glomerular rate (eGFR) equation to measure kidney function despite the development of more up-to-date methods. In this study, we developed two sets of DL models using fundus images from the UK Biobank to ascertain the effects of using a creatinine and cystatin-C eGFR equation over the baseline creatinine-only eGFR equation on fundus image-based DL CKD predictors. Our results show that a creatinine and cystatin-C eGFR significantly improved classification performance over the baseline creatinine-only eGFR when the models were evaluated conventionally. However, these differences were no longer significant when the models were assessed on clinical labels based on ICD10. Furthermore, we also observed variations in model performance and systemic condition incidence between our study and the ones conducted previously. We hypothesize that limitations in existing eGFR equations and the paucity of retinal features uniquely indicative of CKD may contribute to these inconsistencies. These findings emphasize the need for developing more transparent models to facilitate a better understanding of the mechanisms underpinning the ability of DL models to detect CKD from fundus images.
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Affiliation(s)
- Songyang An
- School of Optometry and Vision Science, The University of Auckland, Auckland, New Zealand
- Toku Eyes Limited NZ, Auckland, New Zealand
| | - Ehsan Vaghefi
- School of Optometry and Vision Science, The University of Auckland, Auckland, New Zealand
- Toku Eyes Limited NZ, Auckland, New Zealand
| | - Song Yang
- Toku Eyes Limited NZ, Auckland, New Zealand
| | - Li Xie
- Toku Eyes Limited NZ, Auckland, New Zealand
| | - David Squirrell
- Toku Eyes Limited NZ, Auckland, New Zealand
- Auckland District Health Board, Auckland, New Zealand
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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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Betzler BK, Chee EYL, He F, Lim CC, Ho J, Hamzah H, Tan NC, Liew G, McKay GJ, Hogg RE, Young IS, Cheng CY, Lim SC, Lee AY, Wong TY, Lee ML, Hsu W, Tan GSW, Sabanayagam C. Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes. J Am Med Inform Assoc 2023; 30:1904-1914. [PMID: 37659103 PMCID: PMC10654858 DOI: 10.1093/jamia/ocad179] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/17/2023] [Accepted: 08/21/2023] [Indexed: 09/04/2023] Open
Abstract
OBJECTIVE To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations. MATERIALS AND METHODS We trained 3 models: (1) image-only; (2) risk factor (RF)-only multivariable logistic regression (LR) model adjusted for age, sex, ethnicity, diabetes duration, HbA1c, systolic blood pressure; (3) hybrid multivariable LR model combining RF data and standardized z-scores from image-only model. Data from Singapore Integrated Diabetic Retinopathy Program (SiDRP) were used to develop (6066 participants with diabetes, primary-care-based) and internally validate (5-fold cross-validation) the models. External testing on 2 independent datasets: (1) Singapore Epidemiology of Eye Diseases (SEED) study (1885 participants with diabetes, population-based); (2) Singapore Macroangiopathy and Microvascular Reactivity in Type 2 Diabetes (SMART2D) (439 participants with diabetes, cross-sectional) in Singapore. Supplementary external testing on 2 Caucasian cohorts: (3) Australian Eye and Heart Study (AHES) (460 participants with diabetes, cross-sectional) and (4) Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) (265 participants with diabetes, cross-sectional). RESULTS In SiDRP validation, area under the curve (AUC) was 0.826(95% CI 0.818-0.833) for image-only, 0.847(0.840-0.854) for RF-only, and 0.866(0.859-0.872) for hybrid. Estimates with SEED were 0.764(0.743-0.785) for image-only, 0.802(0.783-0.822) for RF-only, and 0.828(0.810-0.846) for hybrid. In SMART2D, AUC was 0.726(0.686-0.765) for image-only, 0.701(0.660-0.741) in RF-only, 0.761(0.724-0.797) for hybrid. DISCUSSION AND CONCLUSION There is potential for DLA using retinal images as a screening adjunct for DKD among individuals with diabetes. This can value-add to existing DLA systems which diagnose diabetic retinopathy from retinal images, facilitating primary screening for DKD.
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Affiliation(s)
- Bjorn Kaijun Betzler
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Evelyn Yi Lyn Chee
- School of Computing, National University of Singapore, 117417, Singapore
| | - Feng He
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
| | - Cynthia Ciwei Lim
- Department of Renal Medicine, Singapore General Hospital, 168753, Singapore
| | - Jinyi Ho
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
| | - Haslina Hamzah
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
| | - Ngiap Chuan Tan
- SingHealth Polyclinics, Singapore Health Services, 168582, Singapore
| | - Gerald Liew
- Westmead Institute for Medical Research, University of Sydney, NSW 2145, Australia
| | - Gareth J McKay
- Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom
| | - Ruth E Hogg
- Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom
| | - Ian S Young
- Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
| | - Su Chi Lim
- Khoo Teck Puat Hospital, 768828, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, 117549, Singapore
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA 98104, United States
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
| | - Mong Li Lee
- School of Computing, National University of Singapore, 117417, Singapore
| | - Wynne Hsu
- School of Computing, National University of Singapore, 117417, Singapore
| | - Gavin Siew Wei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
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Liu Y, Zhang F, Gao X, Liu T, Dong J. Lesion-aware attention network for diabetic nephropathy diagnosis with optical coherence tomography images. Front Med (Lausanne) 2023; 10:1259478. [PMID: 37964881 PMCID: PMC10641799 DOI: 10.3389/fmed.2023.1259478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 10/11/2023] [Indexed: 11/16/2023] Open
Abstract
Purpose For early screening of diabetic nephropathy patients, we propose a deep learning algorithm to screen high-risk patients with diabetic nephropathy from retinal images of diabetic patients. Methods We propose the use of attentional mechanisms to improve the model's focus on lesion-prone regions of retinal OCT images. First, the data is trained using the base network and the Grad-CAM algorithm locates image regions that have a large impact on the model output and generates a rough mask localization map. The mask is used as a auxiliary region to realize the auxiliary attention module. We then inserted the region-guided attention module into the baseline model and trained the CNN model to guide the model to better focus on relevant lesion features. The proposed model improves the recognition of the lesion region. Results To evaluate the lesion-aware attention network, we trained and tested it using OCT volumetric data collected from 66 patients with diabetic retinal microangiopathy (89 eyes, male = 43, female = 23). There were 45 patients (60 eyes, male=27, female = 18) in DR group and 21 patients (29 eyes, male = 16, female = 5) in DN group. Our proposed model performs even better in disease classification, specifically, the accuracy of the proposed model was 91.68%, the sensitivity was 89.99%, and the specificity was 92.18%. Conclusion The proposed lesion-aware attention model can provide reliable screening of high-risk patients with diabetic nephropathy.
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Affiliation(s)
- Yuliang Liu
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, Jinan, China
| | - Fenghang Zhang
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, Jinan, China
| | - Xizhan Gao
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, Jinan, China
| | - Tingting Liu
- Shandong Eye Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jiwen Dong
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, Jinan, China
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Hu W, Yii FSL, Chen R, Zhang X, Shang X, Kiburg K, Woods E, Vingrys A, Zhang L, Zhu Z, He M. A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images. Transl Vis Sci Technol 2023; 12:14. [PMID: 37440249 PMCID: PMC10353749 DOI: 10.1167/tvst.12.7.14] [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: 03/24/2023] [Accepted: 06/08/2023] [Indexed: 07/14/2023] Open
Abstract
Purpose The purpose of this study was to perform a systematic review and meta-analysis to synthesize evidence from studies using deep learning (DL) to predict cardiovascular disease (CVD) risk from retinal images. Methods A systematic literature search was performed in MEDLINE, Scopus, and Web of Science up to June 2022. We extracted data pertaining to predicted outcomes, model development, and validation and model performance metrics. Included studies were graded using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. Model performance was pooled across eligible studies using a random-effects meta-analysis model. Results A total of 26 studies were included in the analysis. There were 42 CVD risk-related outcomes predicted from retinal images were identified, including 33 CVD risk factors, 4 cardiac imaging biomarkers, 2 CVD risk scores, the presence of CVD, and incident CVD. Three studies that aimed to predict the development of future CVD events reported an area under the receiver operating curve (AUROC) between 0.68 and 0.81. Models that used retinal images as input data had a pooled mean absolute error of 3.19 years (95% confidence interval [CI] = 2.95-3.43) for age prediction; a pooled AUROC of 0.96 (95% CI = 0.95-0.97) for gender classification; a pooled AUROC of 0.80 (95% CI = 0.73-0.86) for diabetes detection; and a pooled AUROC of 0.86 (95% CI = 0.81-0.92) for the detection of chronic kidney disease. We observed a high level of heterogeneity and variation in study designs. Conclusions Although DL models appear to have reasonably good performance when it comes to predicting CVD risk, further work is necessary to evaluate the real-world applicability and predictive accuracy. Translational Relevance DL-based CVD risk assessment from retinal images holds great promise to be translated to clinical practice as a novel approach for CVD risk assessment, given its simple, quick, and noninvasive nature.
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Affiliation(s)
- Wenyi Hu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Fabian S. L. Yii
- Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
- Curle Ophthalmology Laboratory, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
| | - Ruiye Chen
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
| | - Xinyu Zhang
- Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xianwen Shang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Katerina Kiburg
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Ekaterina Woods
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Algis Vingrys
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, Australia
| | - Lei Zhang
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia
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9
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Tan Y, Sun X. Ocular images-based artificial intelligence on systemic diseases. Biomed Eng Online 2023; 22:49. [PMID: 37208715 DOI: 10.1186/s12938-023-01110-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/02/2023] [Indexed: 05/21/2023] Open
Abstract
PURPOSE To provide a summary of the research advances on ocular images-based artificial intelligence on systemic diseases. METHODS Narrative literature review. RESULTS Ocular images-based artificial intelligence has been used in a variety of systemic diseases, including endocrine, cardiovascular, neurological, renal, autoimmune, and hematological diseases, and many others. However, the studies are still at an early stage. The majority of studies have used AI only for diseases diagnosis, and the specific mechanisms linking systemic diseases to ocular images are still unclear. In addition, there are many limitations to the research, such as the number of images, the interpretability of artificial intelligence, rare diseases, and ethical and legal issues. CONCLUSION While ocular images-based artificial intelligence is widely used, the relationship between the eye and the whole body should be more clearly elucidated.
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Affiliation(s)
- Yuhe Tan
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xufang Sun
- Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
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10
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Wen J, Liu D, Wu Q, Zhao L, Iao WC, Lin H. Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives. VIEW 2023. [DOI: 10.1002/viw.20220070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2023] Open
Affiliation(s)
- Jingyi Wen
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Dong Liu
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Qianni Wu
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Lanqin Zhao
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Wai Cheng Iao
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
| | - Haotian Lin
- State Key Laboratory of OphthalmologyZhongshan Ophthalmic CenterSun Yat‐sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Disease GuangzhouChina
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics Zhongshan School of Medicine Sun Yat‐sen University Guangzhou China
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11
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Iao WC, Zhang W, Wang X, Wu Y, Lin D, Lin H. Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13050900. [PMID: 36900043 PMCID: PMC10001234 DOI: 10.3390/diagnostics13050900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/16/2023] [Accepted: 02/18/2023] [Indexed: 03/06/2023] Open
Abstract
Deep learning (DL) is the new high-profile technology in medical artificial intelligence (AI) for building screening and diagnosing algorithms for various diseases. The eye provides a window for observing neurovascular pathophysiological changes. Previous studies have proposed that ocular manifestations indicate systemic conditions, revealing a new route in disease screening and management. There have been multiple DL models developed for identifying systemic diseases based on ocular data. However, the methods and results varied immensely across studies. This systematic review aims to summarize the existing studies and provide an overview of the present and future aspects of DL-based algorithms for screening systemic diseases based on ophthalmic examinations. We performed a thorough search in PubMed®, Embase, and Web of Science for English-language articles published until August 2022. Among the 2873 articles collected, 62 were included for analysis and quality assessment. The selected studies mainly utilized eye appearance, retinal data, and eye movements as model input and covered a wide range of systemic diseases such as cardiovascular diseases, neurodegenerative diseases, and systemic health features. Despite the decent performance reported, most models lack disease specificity and public generalizability for real-world application. This review concludes the pros and cons and discusses the prospect of implementing AI based on ocular data in real-world clinical scenarios.
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Affiliation(s)
- Wai Cheng Iao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Weixing Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Xun Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Yuxuan Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou 570311, China
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510060, China
- Correspondence:
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Lim WS, Ho HY, Ho HC, Chen YW, Lee CK, Chen PJ, Lai F, Jang JSR, Ko ML. Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: focus group study on high prevalence of myopia. BMC Med Imaging 2022; 22:206. [PMID: 36434508 PMCID: PMC9700928 DOI: 10.1186/s12880-022-00933-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/10/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Glaucoma is one of the major causes of blindness; it is estimated that over 110 million people will be affected by glaucoma worldwide by 2040. Research on glaucoma detection using deep learning technology has been increasing, but the diagnosis of glaucoma in a large population with high incidence of myopia remains a challenge. This study aimed to provide a decision support system for the automatic detection of glaucoma using fundus images, which can be applied for general screening, especially in areas of high incidence of myopia. METHODS A total of 1,155 fundus images were acquired from 667 individuals with a mean axial length of 25.60 ± 2.0 mm at the National Taiwan University Hospital, Hsinchu Br. These images were graded based on the findings of complete ophthalmology examinations, visual field test, and optical coherence tomography into three groups: normal (N, n = 596), pre-perimetric glaucoma (PPG, n = 66), and glaucoma (G, n = 493), and divided into a training-validation (N: 476, PPG: 55, G: 373) and test (N: 120, PPG: 11, G: 120) sets. A multimodal model with the Xception model as image feature extraction and machine learning algorithms [random forest (RF), support vector machine (SVM), dense neural network (DNN), and others] was applied. RESULTS The Xception model classified the N, PPG, and G groups with 93.9% of the micro-average area under the receiver operating characteristic curve (AUROC) with tenfold cross-validation. Although normal and glaucoma sensitivity can reach 93.51% and 86.13% respectively, the PPG sensitivity was only 30.27%. The AUROC increased to 96.4% in the N + PPG and G groups. The multimodal model with the N + PPG and G groups showed that the AUROCs of RF, SVM, and DNN were 99.56%, 99.59%, and 99.10%, respectively; The N and PPG + G groups had less than 1% difference. The test set showed an overall 3%-5% less AUROC than the validation results. CONCLUSION The multimodal model had good AUROC while detecting glaucoma in a population with high incidence of myopia. The model shows the potential for general automatic screening and telemedicine, especially in Asia. TRIAL REGISTRATION The study was approved by the Institutional Review Board of the National Taiwan University Hospital, Hsinchu Branch (no. NTUHHCB 108-025-E).
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Affiliation(s)
- Wee Shin Lim
- grid.19188.390000 0004 0546 0241Department of Computer Science and Information Engineering, National Taiwan University, Taipei City 10617, Taiwan, ROC
| | - Heng-Yen Ho
- grid.19188.390000 0004 0546 0241School of Medicine, National Taiwan University, Taipei City 10617, Taiwan, ROC
| | - Heng-Chen Ho
- grid.19188.390000 0004 0546 0241School of Medicine, National Taiwan University, Taipei City 10617, Taiwan, ROC
| | - Yan-Wu Chen
- grid.412036.20000 0004 0531 9758Department of Applied Mathematics, National Sun Yat-Sen University, Kaohsiung City 804201, Taiwan, ROC
| | - Chih-Kuo Lee
- grid.412094.a0000 0004 0572 7815Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu City 300, Taiwan, ROC
| | - Pao-Ju Chen
- grid.412094.a0000 0004 0572 7815Department of Ophthalmology, National Taiwan University Hospital Hsin-Chu Branch, No. 25, Lane 442, Sec.1, Jingguo Rd., Hsinchu City 300, Taiwan, ROC
| | - Feipei Lai
- grid.19188.390000 0004 0546 0241Department of Electrical Engineering, National Taiwan University, Taipei City 10617, Taiwan, ROC
| | - Jyh-Shing Roger Jang
- grid.19188.390000 0004 0546 0241Department of Computer Science and Information Engineering, National Taiwan University, Taipei City 10617, Taiwan, ROC
| | - Mei-Lan Ko
- grid.412094.a0000 0004 0572 7815Department of Ophthalmology, National Taiwan University Hospital Hsin-Chu Branch, No. 25, Lane 442, Sec.1, Jingguo Rd., Hsinchu City 300, Taiwan, ROC ,grid.38348.340000 0004 0532 0580Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Taipei City 10617, Taiwan, ROC
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Betzler BK, Rim TH, Sabanayagam C, Cheng CY. Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging. Front Digit Health 2022; 4:889445. [PMID: 35706971 PMCID: PMC9190759 DOI: 10.3389/fdgth.2022.889445] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/06/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial Intelligence (AI) analytics has been used to predict, classify, and aid clinical management of multiple eye diseases. Its robust performances have prompted researchers to expand the use of AI into predicting systemic, non-ocular diseases and parameters based on ocular images. Herein, we discuss the reasons why the eye is well-suited for systemic applications, and review the applications of deep learning on ophthalmic images in the prediction of demographic parameters, body composition factors, and diseases of the cardiovascular, hematological, neurodegenerative, metabolic, renal, and hepatobiliary systems. Three main imaging modalities are included—retinal fundus photographs, optical coherence tomographs and external ophthalmic images. We examine the range of systemic factors studied from ophthalmic imaging in current literature and discuss areas of future research, while acknowledging current limitations of AI systems based on ophthalmic images.
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Affiliation(s)
- Bjorn Kaijun Betzler
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Tyler Hyungtaek Rim
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Charumathi Sabanayagam
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
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Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia. Diagnostics (Basel) 2022; 12:diagnostics12030742. [PMID: 35328292 PMCID: PMC8947335 DOI: 10.3390/diagnostics12030742] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/10/2022] [Accepted: 03/16/2022] [Indexed: 12/20/2022] Open
Abstract
Pathologic myopia causes vision impairment and blindness, and therefore, necessitates a prompt diagnosis. However, there is no standardized definition of pathologic myopia, and its interpretation by 3D optical coherence tomography images is subjective, requiring considerable time and money. Therefore, there is a need for a diagnostic tool that can automatically and quickly diagnose pathologic myopia in patients. This study aimed to develop an algorithm that uses 3D optical coherence tomography volumetric images (C-scan) to automatically diagnose patients with pathologic myopia. The study was conducted using 367 eyes of patients who underwent optical coherence tomography tests at the Ophthalmology Department of Incheon St. Mary’s Hospital and Seoul St. Mary’s Hospital from January 2012 to May 2020. To automatically diagnose pathologic myopia, a deep learning model was developed using 3D optical coherence tomography images. The model was developed using transfer learning based on four pre-trained convolutional neural networks (ResNet18, ResNext50, EfficientNetB0, EfficientNetB4). Grad-CAM was used to visualize features affecting the detection of pathologic myopia. The performance of each model was evaluated and compared based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The model based on EfficientNetB4 showed the best performance (95% accuracy, 93% sensitivity, 96% specificity, and 98% AUROC) in identifying pathologic myopia.
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Staffini A, Svensson T, Chung UI, Svensson AK. Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2021; 22:s22010034. [PMID: 35009581 PMCID: PMC8747593 DOI: 10.3390/s22010034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 05/04/2023]
Abstract
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual's age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).
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Affiliation(s)
- Alessio Staffini
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Department of Economics and Finance, Catholic University of Milan, 20123 Milan, Italy
- Business Promotion Division, ALBERT Inc., Tokyo 169-0074, Japan
| | - Thomas Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- School of Health Innovation, Kanagawa University of Human Services Graduate School, Yokosuka 210-0821, Japan
- Department of Clinical Sciences, Lund University, Skåne University Hospital, 221 84 Malmo, Sweden
- Correspondence:
| | - Ung-il Chung
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- School of Health Innovation, Kanagawa University of Human Services Graduate School, Yokosuka 210-0821, Japan
- Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Akiko Kishi Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Department of Clinical Sciences, Lund University, Skåne University Hospital, 221 84 Malmo, Sweden
- Department of Diabetes and Metabolic Diseases, The University of Tokyo, Tokyo 113-8655, Japan
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Abstract
PURPOSE OF REVIEW Systemic retinal biomarkers are biomarkers identified in the retina and related to evaluation and management of systemic disease. This review summarizes the background, categories and key findings from this body of research as well as potential applications to clinical care. RECENT FINDINGS Potential systemic retinal biomarkers for cardiovascular disease, kidney disease and neurodegenerative disease were identified using regression analysis as well as more sophisticated image processing techniques. Deep learning techniques were used in a number of studies predicting diseases including anaemia and chronic kidney disease. A virtual coronary artery calcium score performed well against other competing traditional models of event prediction. SUMMARY Systemic retinal biomarker research has progressed rapidly using regression studies with clearly identified biomarkers such as retinal microvascular patterns, as well as using deep learning models. Future systemic retinal biomarker research may be able to boost performance using larger data sets, the addition of meta-data and higher resolution image inputs.
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Kang EYC, Yeung L, Lee YL, Wu CH, Peng SY, Chen YP, Gao QZ, Lin C, Kuo CF, Lai CC. A Multimodal Imaging-Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study. JMIR Med Inform 2021; 9:e28868. [PMID: 34057419 PMCID: PMC8204240 DOI: 10.2196/28868] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/18/2021] [Accepted: 05/03/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Retinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists. OBJECTIVE The aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging. METHODS This retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve. RESULTS A total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases. CONCLUSIONS Our study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.
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Affiliation(s)
- Eugene Yu-Chuan Kang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ling Yeung
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Ophthalmology, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Yi-Lun Lee
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Cheng-Hsiu Wu
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Ophthalmology, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Shu-Yen Peng
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Ophthalmology, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Yueh-Peng Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Quan-Ze Gao
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Chihung Lin
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Chang-Fu Kuo
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Chi-Chun Lai
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Ophthalmology, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
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