Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China

Zhang, Li; Yuan, Mengya; An, Zhen; Zhao, Xin; Wu, Hui; Li, Haibin; Ya, Wang; Sun, Baoshan; Li, Huijun; Ding, Shibin; Zeng, Xiang; Chao, Ling; Li, Pan; Wu, Weidong

doi:10.1371/journal.pone.0233166

Cited by 54 publications

(52 citation statements)

References 36 publications

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“…Additionally, other risk factors such as age, drinking status, smoking status, salty taste, and body mass index (BMI) were also predicted with accuracies > 70%. These results support the application of deep learning to retinal fundus images in the identification of individuals at risk for CVD [ 30 ].…”

Section: Heart Eye and Aisupporting

confidence: 77%

“…The use of deep learning combined with retinal imaging in the diagnosis of cardiovascular conditions is a relatively new area of research (Table 1 [23][24][25][26][27][28][29][30][31]). In 2007, researchers from Australia, Singapore, and the USA showed that retinopathies obtained from fundus photographs were associated with the presence of any degree of coronary artery calcification (CAC) score > 0, measured by cardiac computed tomography scanning (odds ratio (OR): 1.22; 95% CI: 1.04 -1.43) in a multi-ethnic population without clinical heart disease, after adjustment for multiple variables [23].…”

Section: Retinal Imaging Heart Disease and Deep Learningmentioning

confidence: 99%

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Heart, Eye, and Artificial Intelligence: A Review

Gupta

Reddy

2021

Cardiol Res

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Heart disease continues to be the leading cause of death in the USA. Deep learning-based artificial intelligence (AI) methods have become increasingly common in studying the various factors involved in cardiovascular disease. The usage of retinal scanning techniques to diagnose retinal diseases, such as diabetic retinopathy, age-related macular degeneration, glaucoma and others, using fundus photographs and optical coherence tomography angiography (OCTA) has been extensively documented. Researchers are now looking to combine the power of AI with the non-invasive ease of retinal scanning to examine the workings of the heart and predict changes in the macrovasculature based on microvascular features and function. In this review, we summarize the current state of the field in using retinal imaging to diagnose cardiovascular issues and other diseases.

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Section: Heart Eye and Aisupporting

confidence: 77%

Section: Retinal Imaging Heart Disease and Deep Learningmentioning

confidence: 99%

Heart, Eye, and Artificial Intelligence: A Review

Gupta

Reddy

2021

Cardiol Res

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“…The retinal vasculature features used in the p r e d i c t i o n m o d e l s s h o w e d c o n s i d e r a b l e discrimination. With the assistance of deep learning, retinal images have recently been assessed for corresponding changes under the condition of hypertension 15) , hyperglycemia, and dyslipidemia 16) . Retinal imaging was also considered a potential biomarker in the assessment of cardiovascular diseases.…”

Section: Discussionmentioning

confidence: 99%

“…Retinal vasculature holds sufficient information regarding vascular health [7][8][9][10] , heart failure 11) , hypertension 12) , and subtypes of stroke 13) . With the assistance of deep learning, recent studies have explored the value of retinal imaging in the prediction of cardiovascular risk factors 14) , as well as the changes of retinal microvasculature affected by hypertension 15) and other chronic diseases 16) . Besides, retinal vasculature was found to be associated with CAD, which could provide additional implications on cardiovascular risks coexisting with microvascular dysfunction in patients 17,18) .…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Retinal Vasculature Nomogram in Suspected Angina Due to Coronary Artery Disease

Zhong

Qin

et al. 2022

JAT

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To develop and validate a nomogram using retinal vasculature features and clinical variables to predict coronary artery disease (CAD) in patients with suspected angina. Methods: The prediction model consisting of 795 participants was developed in a training set of 508 participants with suspected angina due to CAD, and data were collected from January 2018 to June 2019. The held-out validation was conducted with 287 consecutive patients from July 2019 to November 2019. All patients with suspected CAD received optical coherence tomography angiography (OCTA) examination before undergoing coronary CT angiography. LASSO regression model was used for data reduction and feature selection. Multivariable logistic regression analysis was used to develop the retinal vasculature model for predicting the probability of the presence of CAD. Results: Three potential OCTA parameters including vessel density of the nasal and temporal perifovea in the superficial capillary plexus and vessel density of the inferior parafovea in the deep capillary plexus were further selected as independent retinal vasculature predictors. Model clinical electrocardiogram (ECG) OCTA (clinical variables ECG OCTA) was presented as the individual prediction nomogram, with good discrimination (AUC of 0.942 [95% CI, 0.923-0.961] and 0.897 [95% CI, 0.861-0.933] in the training and held-out validation sets, respectively) and good calibration. Decision curve analysis indicated the clinical applicability of this retinal vasculature nomogram. Conclusions: The presented retinal vasculature nomogram based on individual probability can accurately identify the presence of CAD, which could improve patient selection and diagnostic yield of aggressive testing before determining a diagnosis.

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“…6 More recently, several studies on deep learning-predicted systemic conditions from retinal photographs have been published. [7][8][9][10][11] Nevertheless, the applications of artificial intelligence and deep learning in this field are in the early stages. The broader capacity of deep learning to predict other systemic biomarkers based on retinal photographs, as well as the generalisation of the trained deep-learning algorithm, 12 remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms

Rim

Lee²,

Kim³

et al. 2020

The Lancet Digital Health

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Background The application of deep learning to retinal photographs has yielded promising results in predicting age, sex, blood pressure, and haematological parameters. However, the broader applicability of retinal photograph-based deep learning for predicting other systemic biomarkers and the generalisability of this approach to various populations remains unexplored. MethodsWith use of 236 257 retinal photographs from seven diverse Asian and European cohorts (two health screening centres in South Korea, the Beijing Eye Study, three cohorts in the Singapore Epidemiology of Eye Diseases study, and the UK Biobank), we evaluated the capacities of 47 deep-learning algorithms to predict 47 systemic biomarkers as outcome variables, including demographic factors (age and sex); body composition measurements; blood pressure; haematological parameters; lipid profiles; biochemical measures; biomarkers related to liver function, thyroid function, kidney function, and inflammation; and diabetes. The standard neural network architecture of VGG16 was adopted for model development. Findings In addition to previously reported systemic biomarkers, we showed quantification of body composition indices (muscle mass, height, and bodyweight) and creatinine from retinal photographs. Body muscle mass could be predicted with an R² of 0•52 (95% CI 0•51-0•53) in the internal test set, and of 0•33 (0•30-0•35) in one external test set with muscle mass measurement available. The R² value for the prediction of height was 0•42 (0•40-0•43), of bodyweight was 0•36 (0•34-0•37), and of creatinine was 0•38 (0•37-0•40) in the internal test set. However, the performances were poorer in external test sets (with the lowest performance in the European cohort), with R² values ranging between 0•08 and 0•28 for height, 0•04 and 0•19 for bodyweight, and 0•01 and 0•26 for creatinine. Of the 47 systemic biomarkers, 37 could not be predicted well from retinal photographs via deep learning (R²≤0•14 across all external test sets).Interpretation Our work provides new insights into the potential use of retinal photographs to predict systemic biomarkers, including body composition indices and serum creatinine, using deep learning in populations with a similar ethnic background. Further evaluations are warranted to validate these findings and evaluate the clinical utility of these algorithms.

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Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China

Cited by 54 publications

References 36 publications

Heart, Eye, and Artificial Intelligence: A Review

Heart, Eye, and Artificial Intelligence: A Review

Development and Validation of Retinal Vasculature Nomogram in Suspected Angina Due to Coronary Artery Disease

Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms

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