Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank

BackgroundCurrently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5-10% (termed as borderline-QRISK3 group) using the UK Biobank.MethodsReti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups.ResultsAmong 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30-1.52) with a 13.1% (95% CI, 11.7-14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010-0.017) in non-statin cohort, 0.013 (0.007-0.019) in stage 1 hypertension cohort, and 0.023 (0.018-0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3.ConclusionsReti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD

Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.

AimsThis study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD.Methods and resultsWe defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively.ConclusionThe retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools

The Influence of Parental Myopia on Children’s Myopia in Different Generations of Parent-Offspring Pairs in South Korea

<p><i>Purpose</i>: To compare the heritabilities of myopia and high myopia across three different generations in Korea. <i>Methods</i>: Parent-offspring pairs of different age groups were included: two parents and their offspring aged 10–19 (“young families”), two parents and their offspring aged 20–29 (“middle-aged families”), and two parents and their offspring aged 30–45 (“older families”) were selected from the 2008–2012 Korea National Health and Nutrition Examination Survey. Variance component methods were used to obtain the heritability estimates for myopia and high myopia using parent-offspring pairs from three generations. Spherical equivalents measured in the right eyes were used. <i>Results</i>: From the 2008–2012 data, 2,716, 1,211, and 477 offspring from 1,807 young, 956 middle-aged, and 434 older families were eligible for the study, respectively. For myopia, the additive genetic portion of phenotypic variance was smaller in the younger families (74.7% in the older families, 48.1% in the middle-aged families, and 40.1% in the young families), and the non-shared environmental portion was greater in the younger families (12.4% in older families, 24.9% in middle-aged families, and 46.5% in young families). In contrast, for high myopia, the additive genetic portion remained roughly constant at approximately 60% in all three generations. <i>Conclusions</i>: With myopia, the environmental portion of the phenotypic variance increased and the additive genetic portion decreased as South Korea became more urbanized. With high myopia, the additive genetic portion remained roughly constant at approximately 60%, despite the urbanization.</p

Retinal Vascular Signs and Cerebrovascular Diseases

Background: Cerebrovascular disease (CeVD), including stroke, is a leading cause of death globally. The retina is an extension of the cerebrum, sharing embryological and vascular pathways. The association between different retinal signs and CeVD has been extensively evaluated. In this review, we summarize recent studies which have examined this association. Evidence acquisition: We searched 6 databases through July 2019 for studies evaluating the link between retinal vascular signs and diseases with CeVD. CeVD was classified into 2 groups: clinical CeVD (including clinical stroke, silent cerebral infarction, cerebral hemorrhage, and stroke mortality), and sub-clinical CeVD (including MRI-defined lacunar infarct and white matter lesions [WMLs]). Retinal vascular signs were classified into 3 groups: classic hypertensive retinopathy (including retinal microaneurysms, retinal microhemorrhage, focal/generalized arteriolar narrowing, cotton-wool spots, and arteriovenous nicking), clinical retinal diseases (including diabetic retinopathy [DR], age-related macular degeneration [AMD], retinal vein occlusion, retinal artery occlusion [RAO], and retinal emboli), and retinal vascular imaging measures (including retinal vessel diameter and geometry). We also examined emerging retinal vascular imaging measures and the use of artificial intelligence (AI) deep learning (DL) techniques. Results: Hypertensive retinopathy signs were consistently associated with clinical CeVD and subclinical CeVD subtypes including subclinical cerebral large artery infarction, lacunar infarction, and WMLs. Some clinical retinal diseases such as DR, retinal arterial and venous occlusion, and transient monocular vision loss are consistently associated with clinical CeVD. There is an increased risk of recurrent stroke immediately after RAO. Less consistent associations are seen with AMD. Retinal vascular imaging using computer assisted, semi-automated software to measure retinal vascular caliber and other parameters (tortuosity, fractal dimension, and branching angle) has shown strong associations to clinical and subclinical CeVD. Other new retinal vascular imaging techniques (dynamic retinal vessel analysis, adaptive optics, and optical coherence tomography angiography) are emerging technologies in this field. Application of AI-DL is expected to detect subclinical retinal changes and discrete retinal features in predicting systemic conditions including CeVD. Conclusions: There is extensive and increasing evidence that a range of retinal vascular signs and disease are closely linked to CeVD, including subclinical and clinical CeVD. New technology including AI-DL will allow further translation to clinical utilization

The incidence and prevalence of pterygium in South Korea: A 10-year population-based Korean cohort study

<div><p>Although numerous population-based studies have reported the prevalences and risk factors for pterygium, information regarding the incidence of pterygium is scarce. This population-based cohort study aimed to evaluate the South Korean incidence and prevalence of pterygium. We retrospectively obtained data from a nationally representative sample of 1,116,364 South Koreans in the Korea National Health Insurance Service National Sample Cohort (NHIS-NSC). The associated sociodemographic factors were evaluated using multivariable Cox regression analysis, and the hazard ratios and confidence intervals were calculated. Pterygium was defined based on the Korean Classification of Diseases code, and surgically removed pterygium was defined as cases that required surgical removal. We identified 21,465 pterygium cases and 8,338 surgically removed pterygium cases during the study period. The overall incidences were 2.1 per 1,000 person-years for pterygium and 0.8 per 1,000 person-years for surgically removed pterygium. Among subjects who were ≥40 years old, the incidences were 4.3 per 1,000 person-years for pterygium and 1.7 per 1,000 person-years for surgically removed pterygium. The overall prevalences were 1.9% for pterygium and 0.6% for surgically removed pterygium, and the prevalences increased to 3.8% for pterygium and 1.4% for surgically removed pterygium among subjects who were ≥40 years old. The incidences of pterygium decreased according to year. The incidence and prevalence of pterygium were highest among 60–79-year-old individuals. Increasing age, female sex, and living in a relatively rural area were associated with increased risks of pterygium and surgically removed pterygium in the multivariable Cox regression analysis. Our analyses of South Korean national insurance claims data revealed a decreasing trend in the incidence of pterygium during the study period.</p></div

Retinal Vascular Signs and Cerebrovascular Diseases

Background: Cerebrovascular disease (CeVD), including stroke, is a leading cause of death globally. The retina is an extension of the cerebrum, sharing embryological and vascular pathways. The association between different retinal signs and CeVD has been extensively evaluated. In this review, we summarize recent studies which have examined this association. Evidence acquisition: We searched 6 databases through July 2019 for studies evaluating the link between retinal vascular signs and diseases with CeVD. CeVD was classified into 2 groups: clinical CeVD (including clinical stroke, silent cerebral infarction, cerebral hemorrhage, and stroke mortality), and sub-clinical CeVD (including MRI-defined lacunar infarct and white matter lesions [WMLs]). Retinal vascular signs were classified into 3 groups: classic hypertensive retinopathy (including retinal microaneurysms, retinal microhemorrhage, focal/generalized arteriolar narrowing, cotton-wool spots, and arteriovenous nicking), clinical retinal diseases (including diabetic retinopathy [DR], age-related macular degeneration [AMD], retinal vein occlusion, retinal artery occlusion [RAO], and retinal emboli), and retinal vascular imaging measures (including retinal vessel diameter and geometry). We also examined emerging retinal vascular imaging measures and the use of artificial intelligence (AI) deep learning (DL) techniques. Results: Hypertensive retinopathy signs were consistently associated with clinical CeVD and subclinical CeVD subtypes including subclinical cerebral large artery infarction, lacunar infarction, and WMLs. Some clinical retinal diseases such as DR, retinal arterial and venous occlusion, and transient monocular vision loss are consistently associated with clinical CeVD. There is an increased risk of recurrent stroke immediately after RAO. Less consistent associations are seen with AMD. Retinal vascular imaging using computer assisted, semi-automated software to measure retinal vascular caliber and other parameters (tortuosity, fractal dimension, and branching angle) has shown strong associations to clinical and subclinical CeVD. Other new retinal vascular imaging techniques (dynamic retinal vessel analysis, adaptive optics, and optical coherence tomography angiography) are emerging technologies in this field. Application of AI-DL is expected to detect subclinical retinal changes and discrete retinal features in predicting systemic conditions including CeVD. Conclusions: There is extensive and increasing evidence that a range of retinal vascular signs and disease are closely linked to CeVD, including subclinical and clinical CeVD. New technology including AI-DL will allow further translation to clinical utilization

Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma

Purpose: To analyze the efficacy of a deep learning (DL)-based artificial intelligence (AI)-based algorithm in detecting the presence of diabetic retinopathy (DR) and glaucoma suspect as compared to the diagnosis by specialists secondarily to explore whether the use of this algorithm can reduce the cross-referral in three clinical settings: a diabetologist clinic, retina clinic, and glaucoma clinic. Methods: This is a prospective observational study. Patients between 35 and 65 years of age were recruited from glaucoma and retina clinics at a tertiary eye care hospital and a physician's clinic. Non-mydriatic fundus photography was performed according to the disease-specific protocols. These images were graded by the AI system and specialist graders and comparatively analyzed. Results: Out of 1085 patients, 362 were seen at glaucoma clinics, 341 were seen at retina clinics, and 382 were seen at physician clinics. The kappa agreement between AI and the glaucoma grader was 85% [95% confidence interval (CI): 77.55–92.45%], and retina grading had 91.90% (95% CI: 87.78–96.02%). The retina grader from the glaucoma clinic had 85% agreement, and the glaucoma grader from the retina clinic had 73% agreement. The sensitivity and specificity of AI glaucoma grading were 79.37% (95% CI: 67.30–88.53%) and 99.45 (95% CI: 98.03–99.93), respectively; DR grading had 83.33% (95 CI: 51.59–97.91) and 98.86 (95% CI: 97.35–99.63). The cross-referral accuracy of DR and glaucoma was 89.57% and 95.43%, respectively. Conclusion: DL-based AI systems showed high sensitivity and specificity in both patients with DR and glaucoma; also, there was a good agreement between the specialist graders and the AI system

Sociodemographic Factors Associated with Pterygium and Surgically Removed Pterygium based on Multivariable Cox Regression (n = 1,116,364).

<p>Sociodemographic Factors Associated with Pterygium and Surgically Removed Pterygium based on Multivariable Cox Regression (n = 1,116,364).</p

Incidence and Prevalence of Clinically Diagnosed Pterygium in South Korea.

<p>(A) Incidences of pterygium (black dot and lines) and surgically removed pterygium (gray dot and lines) per 1,000 person-years according to year. (B) Incidences per 1,000 person-years according to age group and (C) prevalence (%) according to year.</p