2023
DOI: 10.1167/tvst.12.7.14
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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

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… Show more

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Cited by 10 publications
(6 citation statements)
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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%
“…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%
“…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%
“…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%