Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction

Lee, Yeong Chan; Cha, Jiho; Shim, Injeong; Park, Woong-Yang; Kang, Se Woong; Lim, Dong Hui; Won, Hong-Hee

doi:10.1038/s41746-023-00748-4

Cited by 28 publications

(6 citation statements)

References 40 publications

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“…In the hybrid system proposed by Diaz-Pinto et al [ 24 ], retinal images and relevant clinical metadata were analyzed together to estimate cardiac indices—including left ventricular mass and left ventricular end-diastolic volume—and to predict incident myocardial infarction; this model showed an AUC of 0.80. In another study, a multimodal deep learning system was proposed by Lee et al [ 25 ] to predict current CVD and assess its performance against a clinical risk factors data-input model simultaneously. The authors found that the integrated network, which combined two networks using two different modalities and made full use of clinical metadata and fundus images, achieved the best performance, with an AUC of 0.872 in the external validation set.…”

Section: Resultsmentioning

confidence: 99%

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Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

Cao

Grzybowski³

et al. 2023

Healthcare

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The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.

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Section: Resultsmentioning

confidence: 99%

“…Therefore, multimodal and multitask learning architecture should be encouraged to isolate the alternations caused by confounding factors. Furthermore, the integrated input of clinical metadata and ocular images, in most cases, may improve predictive accuracy [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

Cao

Grzybowski³

et al. 2023

Healthcare

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“…33 In a case-control study, Lee et al predicted the prevalence of CVD using a multimodal deep learning model integrating retinal images and traditional risk factors and compared the results to the predictive ability of PCE-ASCVD. 10 They found major performance improvement by using the deep learning model.…”

Section: Discussionmentioning

confidence: 99%

“…Since the review was published, we have identified several relevant articles on the topic. 10,11 Finally, we aim to evaluate the studies from a more clinical perspective (e.g. performance comparison with or added value to existing risk scores).…”

Section: Introductionmentioning

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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“…Another application of AI analysis of fundus images is for the prediction of systemic disorders, specifically cardiovascular disease risk. [ 2 ] Future AI models that collectively analyze fundus imaging, basic cardiovascular examination data, and simple investigations such as ECG may turn out to be good predictors of cardiovascular disease risk.…”

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confidence: 99%

Retina: Current research

Chawla

2023

Indian Journal of Ophthalmology

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Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction

Cited by 28 publications

References 40 publications

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

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

Retina: Current research

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