A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre

Cheung, Carol Y.; Xu, Dejiang; Cheng, Ching‐Yu; Sabanayagam, Charumathi; Tham, Yih‐Chung; Yu, Marco; Rim, Tyler Hyungtaek; Chai, Chew Yian; Gopinath, Bamini; Mitchell, Paul; Poulton, Richie; Moffitt, Terrie E.; Caspi, Avshalom; Yam, Jason C.; Tham, Clement C.; Jonas, Jost B.; Wang, Ya Xing; Song, Su Jeong; Burrell, Louise M; Farouque, Omar; Li, Ling Jun; Tan, Gavin Siew Wei; Ting, Daniel Shu Wei; Hsu, Wynne; Lee, Mong Li; Wong, Tien Yin

doi:10.1038/s41551-020-00626-4

Cited by 193 publications

(158 citation statements)

References 93 publications

Supporting

Mentioning

155

Contrasting

Order By: Relevance

“…Last but not the least, this algorithm might also be adopted in dementia clinical trials, incorporated as inclusion criteria to efficiently target eligible participants, or surrogate outcome which could be observed expediently. 32,39 Previous studies have investigated deep learning algorithm based on fundus photographs for screening cardiovascular diseases and anaemia, [13][14][15]40 our study added novel evidence regarding dementia in this field, potentially facilitating the eventual application of fundus photography for simultaneous screening of multiple diseases in large population-based settings.…”

Section: Discussionmentioning

confidence: 95%

Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score

Hua

Xiong²,

et al. 2021

Preprint

View full text Add to dashboard Cite

Importance: The Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognized tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, effective, convenient and noninvasive tool for screening individuals with high dementia risk in large population-based settings is urgently needed. Objective: To develop and validate a deep learning algorithm using retinal fundus photographs for estimating the CAIDE dementia risk score and identifying individuals with high dementia risk. Design: A deep learning algorithm trained via fundus photographs was developed, validated internally and externally with cross-sectional design. Setting: Population-based. Participants: A health check-up population with 271,864 adults were randomized into a development dataset (95%) and an internal validation dataset (5%). The external validation used data from the Beijing Research on Ageing and Vessel (BRAVE) with 1,512 individuals. Exposures: The estimated CAIDE dementia risk score generated from the algorithm. Main Outcome and Measure: The algorithm performance for identifying individuals with high dementia risk was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval (CI). Results: The study involved 258,305 participants (mean aged 42.1 years, men: 52.7%) in development, 13,559 (mean aged 41.2 years, men: 52.5%) in internal validation, and 1,512 (mean aged 59.8 years, men: 37.1%) in external validation. The adjusted coefficient of determination (R2) between the estimated and actual CAIDE dementia risk score was 0.822 in the internal and 0.300 in the external validations, respectively. The algorithm achieved an AUC of 0.931 (95%CI, 0.922 to 0.939) in the internal validation group and 0.782 (95%CI, 0.749 to 0.815) in the external group. Besides, the estimated CAIDE dementia risk score was significantly associated with both comprehensive cognitive function and specific cognitive domains. Conclusions and Relevance: The present study demonstrated that the deep learning algorithm trained via fundus photographs could well identify individuals with high dementia risk in a population-based setting. Our findings suggest that fundus photography may be utilized as a noninvasive and more expedient method for dementia risk stratification.

show abstract

Section: Discussionmentioning

confidence: 95%

Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score

Hua

Xiong²,

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Software developed for measuring changes with progression of DR and cardiovascular disease (or hypertension) manifesting specifically in the larger retinal vessels include SIVA [9], Vampire [10], and Ivan [11] that analyze vascular branching in color fundus images, which are of lower resolution than FA but more globally available. The analysis is based on the Knudtson-Parr-Hubbard formula for measuring retinal vessel caliber (diameter) equivalents, termed central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE).…”

Section: Vascular Treesmentioning

confidence: 99%

“…The analyses combine semi-automated image-processing algorithms that first extract the binary vascular pattern, followed by some manual processing and grading. Recently, SIVA was updated with new AI/deep learning algorithms to automatically segment binary vascular patterns and generate more accurate measures than the previous semi-automated methods [9]. Healthy and cognitively impaired individuals were distinguished by the larger venular asymmetry factor and D f of SIVA [52].…”

Section: Vascular Treesmentioning

confidence: 99%

“…Background; see www.karger.com/doi/10.1159/000514211 for all online suppl. material) which include REAVER [6], AutoTube [7], AngioTool [8], the retinal-specific SIVA [9], Vampire [10], Ivan [11], and a parafoveal capillary density deviation mapping customized for ocular coherence tomography (OCT)-angiography (OCT-A) [12-14]. For other physiological capabilities, VesSAP was developed to quantify 3D vascular branching in the mouse brain [15] and AngioQuant, endothelial tubes in in vitro culture [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vascular Patterning as Integrative Readout of Complex Molecular and Physiological Signaling by VESsel GENeration Analysis

et al. 2021

View full text Add to dashboard Cite

The molecular signaling cascades that regulate angiogenesis and microvascular remodeling are fundamental to normal development, healthy physiology, and pathologies such as inflammation and cancer. Yet quantifying such complex, fractally branching vascular patterns remains difficult. We review application of NASA’s globally available, freely downloadable VESsel GENeration (VESGEN) Analysis software to numerous examples of 2D vascular trees, networks, and tree-network composites. Upon input of a binary vascular image, automated output includes informative vascular maps and quantification of parameters such as tortuosity, fractal dimension, vessel diameter, area, length, number, and branch point. Previous research has demonstrated that cytokines and therapeutics such as vascular endothelial growth factor, basic fibroblast growth factor (fibroblast growth factor-2), transforming growth factor-beta-1, and steroid triamcinolone acetonide specify unique “fingerprint” or “biomarker” vascular patterns that integrate dominant signaling with physiological response. In vivo experimental examples described here include vascular response to keratinocyte growth factor, a novel vessel tortuosity factor; angiogenic inhibition in humanized tumor xenografts by the anti-angiogenesis drug leronlimab; intestinal vascular inflammation with probiotic protection by <i>Saccharomyces boulardii</i>, and a workflow programming of vascular architecture for 3D bioprinting of regenerative tissues from 2D images. Microvascular remodeling in the human retina is described for astronaut risks in microgravity, vessel tortuosity in diabetic retinopathy, and venous occlusive disease.

show abstract

“…Artificial intelligence (AI) deep learning (DL) algorithms developed for retinal photographs have shown highly accurate detection and diagnosis of major eye diseases (e.g., diabetic retinopathy, 1 – 6 age-related macular degeneration, 7 – 9 glaucoma 10 – 12 ), measurement of retinal vessel caliber, 13 retinal vessel segmentation, 14 , 15 and even estimation of cardiovascular risk factors. 16 – 18 As such, integrating DL algorithms into real-time clinical workflow is a priority to realize the significant potential of AI for clinical diagnosis and disease risk stratification.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning–Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study

Yuen

Ran

Shi

et al. 2021

Trans. Vis. Sci. Tech.

Self Cite

View full text Add to dashboard Cite

Purpose Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left). Methods A total of 21,348 retinal photographs from 1914 subjects from various clinical settings in Hong Kong, Singapore, and the United Kingdom were used for training, internal validation, and external testing for the DL module, developed by two DL-based algorithms (EfficientNet-B0 and MobileNet-V2). Results For image-quality assessment, the pre-diagnosis module achieved area under the receiver operating characteristic curve (AUROC) values of 0.975, 0.999, and 0.987 in the internal validation dataset and the two external testing datasets, respectively. For field-of-view assessment, the module had an AUROC value of 1.000 in all of the datasets. For laterality-of-the-eye assessment, the module had AUROC values of 1.000, 0.999, and 0.985 in the internal validation dataset and the two external testing datasets, respectively. Conclusions Our study showed that this three-in-one DL module for assessing image quality, field of view, and laterality of the eye of retinal photographs achieved excellent performance and generalizability across different centers and ethnicities. Translational Relevance The proposed DL-based pre-diagnosis module realized accurate and automated assessments of image quality, field of view, and laterality of the eye of retinal photographs, which could be further integrated into AI-based models to improve operational flow for enhancing disease screening and diagnosis.

show abstract

A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre

Cited by 193 publications

References 93 publications

Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score

Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score

Vascular Patterning as Integrative Readout of Complex Molecular and Physiological Signaling by VESsel GENeration Analysis

Deep-Learning–Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study

Contact Info

Product

Resources

About