A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images

Hu, Wenyi; Yii, Fabian S L; Chen, Ruiye; Zhang, Xiaoxin; Shang, Xianwen; Kiburg, Katerina; Woods, Ekaterina; Vingrys, Algis J.; Zhang, Lei; Zhu, Zhuoting; He, Miao

doi:10.1167/tvst.12.7.14

Cited by 7 publications

(5 citation statements)

References 64 publications

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“…Another six records were identified in citations and references of the included studies and previous reviews on similar topics. 9,18 In total, 24 studies were included in this scoping review, all of which were published after 2018.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Cardiovascular Markers and Diseases Using Retinal Fundus Images and Deep Learning: A Systematic Scoping Review

Li,

Isaksen,

Lebiecka-Johansen

et al. 2024

Preprint

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Background: Cardiovascular risk prediction models based on sociodemographic factors and traditional clinical measurements have received significant attention. With rapid development in deep learning for image analysis in the last decade and the well-known association between micro- and macrovascular complications, some recent studies focused on the prediction of cardiovascular risk using retinal fundus images. The objective of this scoping review is to identify and describe studies using retinal fundus images and deep learning to predict cardiovascular risk markers and diseases. Methods: We searched MEDLINE and Embase for peer-reviewed articles on 17 November 2023. Abstracts and relevant full-text articles were independently screened by two reviewers. We included studies that used deep learning for the analysis of retinal fundus images to predict cardiovascular risk markers (e.g. blood pressure, coronary artery calcification, intima-media thickness) or cardiovascular diseases (prevalent or incident). Studies that used only predefined characteristics of retinal fundus images (e.g. tortuosity, fractal dimension) were not considered. Study characteristics were extracted by the first author and verified by the senior author. Results are presented using descriptive statistics. Results: We included 24 articles in the review, published between 2018 and 2023. Among these, 21 (88%) were cross-sectional studies and eight (33%) were follow-up studies with outcome of clinical CVD. Five studies included a combination of both designs. Most studies (n=23, 96%) used convolutional neural networks to process images. We found nine (38%) studies that incorporated clinical risk factors in the prediction and four (17%) that compared the results to commonly used clinical risk scores in a prospective setting. Three of these reported improved discriminative performance. External validation of models was rare (n=5, 21%). Only four (17%) studies made their code publicly available. Conclusions: There is an increasing interest in using retinal fundus images in cardiovascular risk assessment. However, there is a need for more prospective studies, comparisons of results to clinical risk scores and models augmented with traditional risk factors. Moreover, more extensive code sharing is necessary to make findings reproducible and more impactful beyond a specific study.

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Section: Resultsmentioning

confidence: 99%

Prediction of Cardiovascular Markers and Diseases Using Retinal Fundus Images and Deep Learning: A Systematic Scoping Review

Li,

Isaksen,

Lebiecka-Johansen

et al. 2024

Preprint

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“…25 , 26 The application of DL algorithms to predict ASCVD risk-related outcomes from retinal images has been comprehensively reviewed by Hu and colleagues. 27 This review revealed that, to date, a heterogeneous array of models have been developed using a variety of different inputs: retinal images only, retinal images + various biodata, and reporting against different cardiac-related outcomes. To date, only 2 other groups have reported the results of a DL model trained and then externally validated to predict the ASCVD 10-year risk from retinal photographs.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a deep-learning model to predict 10-year atherosclerotic cardiovascular disease risk from retinal images using the UK Biobank and EyePACS 10K datasets

Vaghefi,

Squirrell,

Yang

et al. 2024

Cardiovascular Digital Health Journal

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“…The application of DL algorithms to predict ASCVD risk-related outcomes from retinal images has been comprehensively reviewed by Hu et al 35 . This review revealed that to date a heterogeneous array of models have been developed using a variety of different inputs: retinal images only, retinal images + various biodata, and reporting against different cardiac-related outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Regardless of diabetes type and status, the PCE equation currently treats all people living with diabetes as a uniform group and it effectively adds a modifier to the regression equation which serves to elevate their risk score compared to non-diabetics. Recently it has been suggested that the traditional regression-based equations, like the PCE, may overestimate ASCVD risk in many people living with diabetes 32,33 The application of DL algorithms to predict ASCVD risk-related outcomes from retinal images has been comprehensively reviewed by Hu et al 35 . This review revealed that to date a heterogeneous array of models have been developed using a variety of different inputs: retinal images only, retinal images + various biodata, and reporting against different cardiac-related outcomes.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a deep-learning model to predict 10-year ASCVD risk from retinal images using the UK Biobank and EyePACS 10K datasets

Vaghefi,

Squirrell,

Yang

et al. 2023

Preprint

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Background: Atherosclerotic Cardiovascular Disease (ASCVD) is a leading cause of death globally, and early detection of high-risk individuals is essential for initiating timely interventions. The authors aimed to develop and validate a deep learning (DL) model to predict an individual's elevated 10-year ASCVD risk score based on retinal images and limited demographic data. Methods: The study used 89,894 retinal fundus images from 44,176 UK Biobank participants (96% non-Hispanic White, 5% diabetic) to train and test the DL model. The DL model was developed using retinal images plus age, race/ethnicity, and sex at birth to predict an individual's 10-year ASCVD risk score using the Pooled Cohort Equation (PCE) as the ground truth. This model was then tested on the US EyePACS 10K dataset (5.8% Non-Hispanic white 99.9% diabetic), composed of 18,900 images from 8,969 diabetic individuals. Elevated ASCVD risk was defined as a PCE score of ?7.5%. Results: In the UK Biobank internal validation dataset, the DL model achieved area under the receiver operating characteristic curve (AUROC) of 0.89, sensitivity 84%, and specificity 90%, for detecting individuals with elevated ASCVD risk scores. In the EyePACS 10K and with the addition of a regression-derived diabetes modifier, it achieved sensitivity 94%, specificity 72%, mean error -0.2%, and mean absolute error 3.1%. Conclusion: This study demonstrates that DL models using retinal images can provide an additional approach to estimating ASCVD risk, and the value of applying DL models to different external datasets and opportunities about ASCVD risk assessment in patients living with diabetes.

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A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images

Cited by 7 publications

References 64 publications

Prediction of Cardiovascular Markers and Diseases Using Retinal Fundus Images and Deep Learning: A Systematic Scoping Review

Prediction of Cardiovascular Markers and Diseases Using Retinal Fundus Images and Deep Learning: A Systematic Scoping Review

Development and validation of a deep-learning model to predict 10-year atherosclerotic cardiovascular disease risk from retinal images using the UK Biobank and EyePACS 10K datasets

Development and validation of a deep-learning model to predict 10-year ASCVD risk from retinal images using the UK Biobank and EyePACS 10K datasets

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