TY - JOUR
T1 - A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images
AU - Hu, Wenyi
AU - Yii, Fabian
AU - Chen, Ruiye
AU - Zhang, Xinyu
AU - Shang, Xianwen
AU - Kiburg, Katerina
AU - Woods, Ekaterina
AU - Vingrys, Algis
AU - Zhang, Lei
AU - Zhu, Zhuoting
AU - He, Mingguang
N1 - Funding Information:
Supported by Medical Research Future Fund (MRFF; MRFAI000035), NHMRC Investigator Grants (APP1175405 and 2010072) and High-level Talent Flexible Introduction Fund of Guangdong Provincial People’s Hospital (KJ012019530). The sponsor or funding organization had no role in the design or conduct of this research.
Publisher Copyright:
© 2023, Association for Research in Vision and Ophthalmology Inc.. All rights reserved.
PY - 2023/7/13
Y1 - 2023/7/13
N2 - 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.
AB - 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.
KW - Cardiovascular Diseases/diagnostic imaging
KW - Deep Learning
KW - Humans
UR - http://dx.doi.org/10.1167/tvst.12.7.14
U2 - 10.1167/tvst.12.7.14
DO - 10.1167/tvst.12.7.14
M3 - Article
C2 - 37440249
SN - 2164-2591
VL - 12
JO - Translational Vision Science & Technology
JF - Translational Vision Science & Technology
IS - 7
ER -