Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review

We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.

Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop

The accessibility of the retina with the use of non-invasive and relatively low-cost ophthalmic imaging techniques and analytics provides a unique opportunity to improve the detection, diagnosis and monitoring of systemic diseases. The National Heart, Lung, and Blood Institute conducted a workshop in October 2022 to examine this concept. On the basis of the discussions at that workshop, this Roadmap describes current knowledge gaps and new research opportunities to evaluate the relationships between the eye (in particular, retinal biomarkers) and the risk of cardiovascular diseases, including coronary artery disease, heart failure, stroke, hypertension and vascular dementia. Identified gaps include the need to simplify and standardize the capture of high-quality images of the eye by non-ophthalmic health workers and to conduct longitudinal studies using multidisciplinary networks of diverse at-risk populations with improved implementation and methods to protect participant and dataset privacy. Other gaps include improving the measurement of structural and functional retinal biomarkers, determining the relationship between microvascular and macrovascular risk factors, improving multimodal imaging 'pipelines', and integrating advanced imaging with 'omics', lifestyle factors, primary care data and radiological reports, by using artificial intelligence technology to improve the identification of individual-level risk. Future research on retinal microvascular disease and retinal biomarkers might additionally provide insights into the temporal development of microvascular disease across other systemic vascular beds.

Detecting the corneal neovascularisation area using artificial intelligence

AIMS

To create and assess the performance of an artificial intelligence-based image analysis tool for the measurement and quantification of the corneal neovascularisation (CoNV) area.

METHODS

Slit lamp images of patients with CoNV were exported from the electronic medical records and included in the study. An experienced ophthalmologist made manual annotations of the CoNV areas, which were then used to create, train and evaluate an automated image analysis tool that uses deep learning to segment and detect CoNV areas. A pretrained neural network (U-Net) was used and fine-tuned on the annotated images. Sixfold cross-validation was used to evaluate the performance of the algorithm on each subset of 20 images. The main metric for our evaluation was intersection over union (IoU).

RESULTS

The slit lamp images of 120 eyes of 120 patients with CoNV were included in the analysis. Detections of the total corneal area achieved IoU between 90.0% and 95.5% in each fold and those of the non-vascularised area achieved IoU between 76.6% and 82.2%. The specificity for the detection was between 96.4% and 98.6% for the total corneal area and 96.6% and 98.0% for the non-vascularised area.

CONCLUSION

The proposed algorithm showed a high accuracy compared with the measurement made by an ophthalmologist. The study suggests that an automated tool using artificial intelligence may be used for the calculation of the CoNV area from the slit-lamp images of patients with CoNV.

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging

BACKGROUND

Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care.

MAIN TEXT

This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care.

CONCLUSION

AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

Retinal imaging for the assessment of stroke risk: a systematic review

BACKGROUND

Stroke is a leading cause of morbidity and mortality. Retinal imaging allows non-invasive assessment of the microvasculature. Consequently, retinal imaging is a technology which is garnering increasing attention as a means of assessing cardiovascular health and stroke risk.

METHODS

A biomedical literature search was performed to identify prospective studies that assess the role of retinal imaging derived biomarkers as indicators of stroke risk.

RESULTS

Twenty-four studies were included in this systematic review. The available evidence suggests that wider retinal venules, lower fractal dimension, increased arteriolar tortuosity, presence of retinopathy, and presence of retinal emboli are associated with increased likelihood of stroke. There is weaker evidence to suggest that narrower arterioles and the presence of individual retinopathy traits such as microaneurysms and arteriovenous nicking indicate increased stroke risk. Our review identified three models utilizing artificial intelligence algorithms for the analysis of retinal images to predict stroke. Two of these focused on fundus photographs, whilst one also utilized optical coherence tomography (OCT) technology images. The constructed models performed similarly to conventional risk scores but did not significantly exceed their performance. Only two studies identified in this review used OCT imaging, despite the higher dimensionality of this data.

CONCLUSION

Whilst there is strong evidence that retinal imaging features can be used to indicate stroke risk, there is currently no predictive model which significantly outperforms conventional risk scores. To develop clinically useful tools, future research should focus on utilization of deep learning algorithms, validation in external cohorts, and analysis of OCT images.

Effects of firsthand tobacco smoking on retinal vessel caliber: a systematic review and meta-analysis

PURPOSE

To review the effects of firsthand tobacco smoking on central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE) of firsthand tobacco smokers.

METHODS

We performed a search on EMBASE and PubMed for studies up to 15th July 2022. Two independent reviewers selected studies with baseline data of CRAE and CRVE of current smokers, nonsmokers, and former smokers. Initial search identified 893 studies, of which 10 were included in the meta-analysis. Two independent reviewers extracted data from the included studies. The quality of studies was assessed by the Newcastle-Ottawa Scale.

RESULTS

In this meta-analysis, 7431 nonsmokers, 2448 current smokers and 5786 former smokers, as well as 7404 nonsmokers, 2430 current smokers and 5763 former smokers were included in CRAE and CRVE analysis respectively. Nonsmokers had narrower CRVE (Weighted mean difference [WMD], -12.15; 95% CI, -17.33 - -6.96) and CRAE (WMD, -4.77; 95% CI, -7.96 - -1.57) than current smokers, and narrower CRVE (WMD, -3.08; 95% CI, -6.06 - -0.11) than former smokers. Current smokers had wider CRVE (WMD, 10.42; 95% CI, 7.80 - 13.04) and CRAE (WMD, 7.05; 95% CI, 6.65 - 7.46) than former smokers. Subgroup analysis and sensitivity analysis were performed.

CONCLUSION

Firsthand tobacco smoking resulted in wider CRAE and CRVE in current and former smokers, particularly in CRVE, and such changes may not be reversible after smoking cessation. Therefore, retinal vessel caliber may reflect the effects of firsthand tobacco smoking and be used to estimate the risk of cardiovascular diseases.

A Multi-Stage Approach for Cardiovascular Risk Assessment from Retinal Images Using an Amalgamation of Deep Learning and Computer Vision Techniques

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the inherent connection between retinal microvascular changes and systemic vascular health. This study aims to develop a predictive model for the early detection of CVDs by evaluating retinal vascular parameters. This methodology integrates both handcrafted features derived through mathematical computation and retinal vascular patterns extracted by artificial intelligence (AI) models. By combining these approaches, we seek to enhance the accuracy and reliability of CVD risk prediction in individuals. The methodology integrates state-of-the-art computer vision algorithms and AI techniques in a multi-stage architecture to extract relevant features from retinal fundus images. These features encompass a range of vascular parameters, including vessel caliber, tortuosity, and branching patterns. Additionally, a deep learning (DL)-based binary classification model is incorporated to enhance predictive accuracy. A dataset comprising fundus images and comprehensive metadata from the clinical trials conducted is utilized for training and validation. The proposed approach demonstrates promising results in the early prediction of CVD risk factors. The interpretability of the approach is enhanced through visualization techniques that highlight the regions of interest within the fundus images that are contributing to the risk predictions. Furthermore, the validation conducted in the clinical trials and the performance analysis of the proposed approach shows the potential to provide early and accurate predictions. The proposed system not only aids in risk stratification but also serves as a valuable tool for identifying vascular abnormalities that may precede overt cardiovascular events. The approach has achieved an accuracy of 85% and the findings of this study underscore the feasibility and efficacy of leveraging fundus images for cardiovascular risk assessment. As a non-invasive and cost-effective modality, fundus image analysis presents a scalable solution for population-wide screening programs. This research contributes to the evolving landscape of precision medicine by providing an innovative tool for proactive cardiovascular health management. Future work will focus on refining the solution's robustness, exploring additional risk factors, and validating its performance in additional and diverse clinical settings.

Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management

The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

Association between obstructive coronary disease and diabetic retinopathy: Cross-sectional study of coronary angiotomography and multimodal retinal imaging

AIMS

To investigate the association between diabetic retinopathy (DR) and coronary artery disease (CAD) using coronary angiotomography (CCTA) and multimodal retinal imaging (MMRI) with ultra-widefield retinography and optical coherence tomography angiography and structural domain.

METHODS

Single-center, cross-sectional, single-blind. Patients with diabetes who had undergone CCTA underwent MMRI. Uni and multivariate analysis were used to assess the association between CAD and DR and to identify variables independently associated with DR.

RESULTS

We included 171 patients, 87 CAD and 84 non-CAD. Most CAD patients were males (74 % vs 38 %, P < 0.01), insulin users (52 % vs 38 %, p < 0.01) and revascularized (64 %). They had a higher prevalence of DR (48 % vs 22 %, p = 0.01), microaneurysms (25 % vs 13 %, p = 0.04), intraretinal cysts (22 % vs 8 %, p = 0.01) and areas of reduced capillary density (46 % vs 20 %, p < 0.01). CAD patients also had lower mean vascular density (MVD) (15.7 % vs 16.5,%, p = 0.049) and foveal avascular zone (FAZ) circularity (0.64 ± 0.1 vs 0.69 ± 0.1, p = 0.04). There were significant and negative correlations between Duke coronary score and MVD (r = -0.189; p = 0.03) and FAZ circularity (r = -0,206; p = 0.02). CAD, DM duration and insulin use independently associated with DR.

CONCLUSIONS

CAD patients had higher prevalence of DR and lower MVD. CAD, DM duration and insulin use were independently associated with DR.

Performance of deep learning for detection of chronic kidney disease from retinal fundus photographs: A systematic review and meta-analysis

OBJECTIVE

Deep learning has been used to detect chronic kidney disease (CKD) from retinal fundus photographs. We aim to evaluate the performance of deep learning for CKD detection.

METHODS

The original studies in CKD patients detected by deep learning from retinal fundus photographs were eligible for inclusion. PubMed, Embase, the Cochrane Library, and Web of Science were searched up to October 31, 2022. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the risk of bias.

RESULTS

Four studies enrolled 114,860 subjects were included. The pooled sensitivity and specificity were 87.8% (95% confidence interval (CI): 61.6% to 98.3%), and 62.4% (95% CI: 44.9% to 78.7%). The area under the curve (AUC) was 0.864 (95%CI: 0.769, 0.986).

CONCLUSION

Deep learning based on retinal fundus photographs has the ability to detect CKD, but it currently has a lot of room for improvement. It is still a long way from clinical application.

Pregnancy induces retinal microvascular changes indicating cardio-metabolic stress

We performed longitudinal examinations of the arterial retinal microvasculature using Adaptive Optics Retinal Imaging in a 30-year-old healthy woman with twin pregnancy from the 23rd week of gestation (wog) to three days postpartum. Two blinded graders recorded the average wall-to-lumen ratio (WLR) of the examined retinal artery. There was a significant increase in the mean WLR over the course of pregnancy followed by a decreasing WLR from the 37th wog. The demonstrated changes in WLR may be an expression of vascular remodeling and adaptation to volume load which indicates that pregnancy can be viewed as a cardiovascular stress test.

Leuven-Haifa High-Resolution Fundus Image Dataset for Retinal Blood Vessel Segmentation and Glaucoma Diagnosis

The Leuven-Haifa dataset contains 240 disc-centered fundus images of 224 unique patients (75 patients with normal tension glaucoma, 63 patients with high tension glaucoma, 30 patients with other eye diseases and 56 healthy controls) from the University Hospitals of Leuven. The arterioles and venules of these images were both annotated by master students in medicine and corrected by a senior annotator. All senior segmentation corrections are provided as well as the junior segmentations of the test set. An open-source toolbox for the parametrization of segmentations was developed. Diagnosis, age, sex, vascular parameters as well as a quality score are provided as metadata. Potential reuse is envisioned as the development or external validation of blood vessels segmentation algorithms or study of the vasculature in glaucoma and the development of glaucoma diagnosis algorithms. The dataset is available on the KU Leuven Research Data Repository (RDR).

Retinal vessel caliber and cognitive performance: the multi-ethnic study of atherosclerosis (MESA)

Retinal vessel calibers share anatomic and physiologic characteristics with the cerebral vasculature and can be visualized noninvasively. In light of the known microvascular contributions to brain health and cognitive function, we aimed to determine if, in a community based-study, retinal vessel calibers and change in caliber over 8 years are associated with cognitive function or trajectory. Participants in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort who completed cognitive testing at Exam 5 (2010-2012) and had retinal vascular caliber measurements (Central Retinal Artery and Vein Equivalents; CRAE and CRVE) at Exam 2 (2002-2004) and Exam 5 were included. Using multivariable linear regression, we evaluated the association of CRAE and CRVE from Exam 2 and Exam 5 and their change between the two exams with scores on tests of global cognitive function (Cognitive Abilities Screening Instrument; CASI), processing speed (Digit Symbol Coding; DSC) and working memory (Digit Span; DS) at Exam 5 and with subsequent change in cognitive scores between Exam 5 and Exam 6 (2016-2018).The main effects are reported as the difference in cognitive test score per SD increment in retinal vascular caliber with 95% confidence intervals (CI). A total of 4334 participants (aged 61.6 ± 9.2 years; 53% female; 41% White) completed cognitive testing and at least one retinal assessment. On multivariable analysis, a 1 SD larger CRAE at exam 5 was associated with a lower concomitant CASI score (- 0.24, 95% CI - 0.46, - 0.02). A 1 SD larger CRVE at exam 2 was associated with a lower subsequent CASI score (- 0.23, 95%CI - 0.45, - 0.01). A 1 SD larger CRVE at exam 2 or 5 was associated with a lower DSC score [(- 0.56, 95% CI - 1.02, - 0.09) and - 0.55 (95% CI - 1.03, - 0.07) respectively]. The magnitude of the associations was relatively small (2.8-3.1% of SD). No significant associations were found between retinal vessel calibers at Exam 2 and 5 with the subsequent score trajectory of cognitive tests performance over an average of 6 years. Wider retinal venular caliber was associated with concomitant and future measures of slower processing speed but not with later cognitive trajectory. Future studies should evaluate the utility of these measures in risk stratification models from a clinical perspective as well as for screening on a population level.

AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.

Automated Retinal Vessel Analysis Based on Fundus Photographs as a Predictor for Non-Ophthalmic Diseases-Evolution and Perspectives

The study of retinal vessels in relation to cardiovascular risk has a long history. The advent of a dedicated tool based on digital imaging, i.e., the retinal vessel analyzer, and also other software such as Integrative Vessel Analysis (IVAN), Singapore I Vessel Assessment (SIVA), and Vascular Assessment and Measurement Platform for Images of the Retina (VAMPIRE), has led to the accumulation of a formidable body of evidence regarding the prognostic value of retinal vessel analysis (RVA) for cardiovascular and cerebrovascular disease (including arterial hypertension in children). There is also the potential to monitor the response of retinal vessels to therapies such as physical activity or bariatric surgery. The dynamic vessel analyzer (DVA) remains a unique way of studying neurovascular coupling, helping to understand the pathogenesis of cerebrovascular and neurodegenerative conditions and also being complementary to techniques that measure macrovascular dysfunction. Beyond cardiovascular disease, retinal vessel analysis has shown associations with and prognostic value for neurological conditions, inflammation, kidney function, and respiratory disease. Artificial intelligence (AI) (represented by algorithms such as QUantitative Analysis of Retinal vessel Topology and siZe (QUARTZ), SIVA-DLS (SIVA-deep learning system), and many others) seems efficient in extracting information from fundus photographs, providing prognoses of various general conditions with unprecedented predictive value. The future challenges will be integrating RVA and other qualitative and quantitative risk factors in a unique, comprehensive prediction tool, certainly powered by AI, while building the much-needed acceptance for such an approach inside the medical community and reducing the "black box" effect, possibly by means of saliency maps.

Coronary artery tortuosity and mid-term all-cause mortality of patients with ischemia and non-obstructive coronary arteries

Background: Coronary artery tortuosity (CAT) is a frequently encountered angiographic feature of patients with ischemia and non-obstructive coronary arteries (INOCA). However, there is limited data regarding the possible correlation between CAT and all-cause mortality in these patients. Aim: To assess the survival prognostic implications of CAT in INOCA patients and the predictors of all-cause mid-term mortality of these patients. Methods: All consecutive INOCA patients, with preserved ejection fraction evaluated for clinical ischemia by coronary angiography in our department between January 2014 and December 2020 were considered for inclusion. Patients with epicardial coronary artery stenosis ≥ 50%, severe pulmonary hypertension, or decompensated extra cardiac disease were excluded. Eleid classification was used for CAT severity characterization. We assessed all-cause mortality in January 2023. Results: Our sample included 328 INOCA patients. 15.54% died during the mean follow-up of 3.75 ± 1.32 years. 79.88% had CAT. CAT patients were older (65.10±9.09 versus 61.24±10.02 years, p=0.002), and more often female (67.18% versus 31.82%, p<0.001). CAT was inversely correlated with all-cause mid-term mortality (OR 0.35, 95%CI 0.16 - 0.77, p=0.01). CAT severity had no impact on survival. In CAT patients the initial multivariable analysis identified NT-proBNP levels (HR 3.96, p=0.01), diabetes mellitus (DM) (HR 4.76, p=0.003), and atrial fibrillation (HR 2.68, p=0.06) as independent predictors of all-cause mortality. In the final analysis, NT-proBNP and DM were the main independent predictors of survival. Conclusions : In our INOCA cohort, CAT patients were older and more likely female. CAT was inversely correlated with mid-term all-cause mortality. NT-proBNP and DM were the main independent predictors of mortality of CAT patients.

Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting

PURPOSE

To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) software in detecting retinal features from retinal images captured by health care professionals in a Danish high street optician chain, compared with test performance from other large population studies (i.e., UK Biobank) where retinal images were captured by non-experts.

METHOD

The dataset FOREVERP (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability, Pilot) contains retinal images obtained from a Danish high street optician chain. The QUARTZ algorithm utilizes both image processing and machine learning methods to determine retinal image quality, vessel segmentation, vessel width, vessel classification (arterioles or venules), and optic disc localization. Outcomes were evaluated by metrics including sensitivity, specificity, and accuracy and compared to human expert ground truths.

RESULTS

QUARTZ's performance was evaluated on a subset of 3,682 images from the FOREVERP database. 80.55% of the FOREVERP images were labelled as being of adequate quality compared to 71.53% of UK Biobank images, with a vessel segmentation sensitivity of 74.64% and specificity of 98.41% (FOREVERP) compared with a sensitivity of 69.12% and specificity of 98.88% (UK Biobank). The mean (± standard deviation) vessel width of the ground truth was 16.21 (4.73) pixels compared to that predicted by QUARTZ of 17.01 (4.49) pixels, resulting in a difference of -0.8 (1.96) pixels. The differences were stable across a range of vessels. The detection rate for optic disc localisation was similar for the two datasets.

CONCLUSION

QUARTZ showed high performance when evaluated on the FOREVERP dataset, and demonstrated robustness across datasets, providing validity to direct comparisons and pooling of retinal feature measures across data sources.

Ethnicity is not biology: retinal pigment score to evaluate biological variability from ophthalmic imaging using machine learning

Background

Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as an inappropriate marker for biological variability.

Methods

We derived a continuous, measured metric, the retinal pigment score (RPS), that quantifies the degree of pigmentation from a colour fundus photograph of the eye. RPS was validated using two large epidemiological studies with demographic and genetic data (UK Biobank and EPIC-Norfolk Study).

Findings

A genome-wide association study (GWAS) of RPS from UK Biobank identified 20 loci with known associations with skin, iris and hair pigmentation, of which 8 were replicated in the EPIC-Norfolk cohort. There was a strong association between RPS and ethnicity, however, there was substantial overlap between each ethnicity and the respective distributions of RPS scores.

Interpretation

RPS serves to decouple traditional demographic variables, such as ethnicity, from clinical imaging characteristics. RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. The code to derive RPS is publicly available at: https://github.com/uw-biomedical-ml/retinal-pigmentation-score.

Funding

The authors did not receive support from any organisation for the submitted work.

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

INTRODUCTION

Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice.

AREAS COVERED

In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP.

EXPERT OPINION

The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.

Cohort profile: rationale and methods of UK Biobank repeat imaging study eye measures to study dementia

PURPOSE

The retina provides biomarkers of neuronal and vascular health that offer promising insights into cognitive ageing, mild cognitive impairment and dementia. This article described the rationale and methodology of eye and vision assessments with the aim of supporting the study of dementia in the UK Biobank Repeat Imaging study.

PARTICIPANTS

UK Biobank is a large-scale, multicentre, prospective cohort containing in-depth genetic, lifestyle, environmental and health information from half a million participants aged 40-69 enrolled in 2006-2010 across the UK. A subset (up to 60 000 participants) of the cohort will be invited to the UK Biobank Repeat Imaging Study to collect repeated brain, cardiac and abdominal MRI scans, whole-body dual-energy X-ray absorptiometry, carotid ultrasound, as well as retinal optical coherence tomography (OCT) and colour fundus photographs.

FINDINGS TO DATE

UK Biobank has helped make significant advances in understanding risk factors for many common diseases, including for dementia and cognitive decline. Ophthalmic genetic and epidemiology studies have also benefited from the unparalleled combination of very large numbers of participants, deep phenotyping and longitudinal follow-up of the cohort, with comprehensive health data linkage to disease outcomes. In addition, we have used UK Biobank data to describe the relationship between retinal structures, cognitive function and brain MRI-derived phenotypes.

FUTURE PLANS

The collection of eye-related data (eg, OCT), as part of the UK Biobank Repeat Imaging study, will take place in 2022-2028. The depth and breadth and longitudinal nature of this dataset, coupled with its open-access policy, will create a major new resource for dementia diagnostic discovery and to better understand its association with comorbid diseases. In addition, the broad and diverse data available in this study will support research into ophthalmic diseases and various other health outcomes beyond dementia.

Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland

AIMS

This study's objective was to evaluate whether deep learning (DL) on retinal photographs from a diabetic retinopathy screening programme improve prediction of incident cardiovascular disease (CVD).

METHODS

DL models were trained to jointly predict future CVD risk and CVD risk factors and used to output a DL score. Poisson regression models including clinical risk factors with and without a DL score were fitted to study cohorts with 2,072 and 38,730 incident CVD events in type 1 (T1DM) and type 2 diabetes (T2DM) respectively.

RESULTS

DL scores were independently associated with incident CVD with adjusted standardised incidence rate ratios of 1.14 (P = 3 × 10^-04 95 % CI (1.06, 1.23)) and 1.16 (P = 4 × 10^-33 95 % CI (1.13, 1.18)) in T1DM and T2DM cohorts respectively. The differences in predictive performance between models with and without a DL score were statistically significant (differences in test log-likelihood 6.7 and 51.1 natural log units) but the increments in C-statistics from 0.820 to 0.822 and from 0.709 to 0.711 for T1DM and T2DM respectively, were small.

CONCLUSIONS

These results show that in people with diabetes, retinal photographs contain information on future CVD risk. However for this to contribute appreciably to clinical prediction of CVD further approaches, including exploitation of serial images, need to be evaluated.

Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review

The healthcare burden of cardiovascular diseases remains a major issue worldwide. Understanding the underlying mechanisms and improving identification of people with a higher risk profile of systemic vascular disease through noninvasive examinations is crucial. In ophthalmology, retinal vascular network imaging is simple and noninvasive and can provide in vivo information of the microstructure and vascular health. For more than 10 years, different research teams have been working on developing software to enable automatic analysis of the retinal vascular network from different imaging techniques (retinal fundus photographs, OCT angiography, adaptive optics, etc.) and to provide a description of the geometric characteristics of its arterial and venous components. Thus, the structure of retinal vessels could be considered a witness of the systemic vascular status. A new approach called "oculomics" using retinal image datasets and artificial intelligence algorithms recently increased the interest in retinal microvascular biomarkers. Despite the large volume of associated research, the role of retinal biomarkers in the screening, monitoring, or prediction of systemic vascular disease remains uncertain. A PubMed search was conducted until August 2022 and yielded relevant peer-reviewed articles based on a set of inclusion criteria. This literature review is intended to summarize the state of the art in oculomics and cardiovascular disease research.