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, Ehsan; Squirrell, David; Yang, Song; An, Songyang; Xie, Li; Durbin, Mary K.; Hou, Huiyuan; Marshall, John; Shreibati, Jacqueline; McConnell, Michael V.; Budoff, Matthew

doi:10.1016/j.cvdhj.2023.12.004

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2024

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Cited by 7 publications

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Color Fundus Photography and Deep Learning Applications in Alzheimer Disease

Dumitrascu,

Li,

Zhu

et al. 2024

Mayo Clinic Proceedings: Digital Health

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Color Fundus Photography and Deep Learning Applications in Alzheimer Disease

Dumitrascu,

Li,

Zhu

et al. 2024

Mayo Clinic Proceedings: Digital Health

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Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs

Martin,

Cook,

Frost

et al. 2024

Eye

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Background/Objectives Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening potential arising from signals persisting despite training and/or ambiguous signals such as from biomarker overlap or high comorbidity. The study aimed to explore the potential to detect clinically useful incidental ocular biomarkers by screening fundus photographs of hypertensive adults using diabetic deep learning algorithms. Subjects/Methods Patients referred for treatment-resistant hypertension were imaged at a hospital unit in Perth, Australia, between 2016 and 2022. The same 45° colour fundus photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. Two expert retinal specialists graded all false-positive results for diabetic retinopathy in non-diabetic participants. Results Of the 29 non-diabetic participants misclassified as positive for diabetic retinopathy, 28 (97%) had clinically useful retinal biomarkers. The models designed to screen for fewer diseases captured more incidental disease. All three algorithms showed a positive correlation between severity of hypertensive retinopathy and misclassified diabetic retinopathy. Conclusions The results suggest that diabetic deep learning models may be responsive to hypertensive and other clinically useful retinal biomarkers within an at-risk, hypertensive cohort. Observing that models trained for fewer diseases captured more incidental pathology increases confidence in signalling hypotheses aligned with using self-supervised learning to develop autonomous comprehensive screening. Meanwhile, non-referable and false-positive outputs of other deep learning screening models could be explored for immediate clinical use in other populations.

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Validation of neuron activation patterns for artificial intelligence models in oculomics

An,

Squirrell

2024

Sci Rep

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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

Cited by 7 publications

References 31 publications

Color Fundus Photography and Deep Learning Applications in Alzheimer Disease

Color Fundus Photography and Deep Learning Applications in Alzheimer Disease

Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs

Validation of neuron activation patterns for artificial intelligence models in oculomics

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