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To evaluate the performance of machine learning (ML) algorithms and to compare them with logistic regression for the prediction of risk of cardiovascular diseases (CVDs), chronic kidney disease (CKD), diabetes (DM), and hypertension (HTN) and in a prospective cohort study using simple clinical predictors. We conducted analyses in a population-based cohort study in Asian adults (n = 6,762). Five different ML models were considered—single-hidden-layer neural network, support vector machine, random forest, gradient boosting machine, and k-nearest neighbor—and were compared with standard logistic regression. The incidences at 6 years of CVD, CKD, DM, and HTN cases were 4.0%, 7.0%, 9.2%, and 34.6%, respectively. Logistic regression reached the highest area under the receiver operating characteristic curve for CKD (0.905 [0.88, 0.93]) and DM (0.768 [0.73, 0.81]) predictions. For CVD and HTN, the best models were neural network (0.753 [0.70, 0.81]) and support vector machine (0.780 [0.747, 0.812]), respectively. However, the differences with logistic regression were small (less than 1%) and nonsignificant. Logistic regression, gradient boosting machine, and neural network were systematically ranked among the best models. Logistic regression yields as good performance as ML models to predict the risk of major chronic diseases with low incidence and simple clinical predictors. •Low-dimensional settings include low number of events and predictors.•In such settings, logistic regression yields as good performance as ML models.•ML techniques may not be warranted in such cases.

BackgroundThe impacts of social restrictions for COVID-19 on children’s vision and lifestyle remain unknown.AimsTo investigate myopia incidence, spherical equivalent refraction (SER) and lifestyle changes among schoolchildren during the COVID-19 pandemic.MethodsTwo separate longitudinal cohorts of children aged 6–8 years in Hong Kong were included. The COVID-19 cohort was recruited at the beginning of the COVID-19 outbreak, whereas the pre-COVID-19 cohort was recruited before the COVID-19 pandemic. All children received ocular examinations, and answered a standardised questionnaire relating to their lifestyle, including time spent on outdoor activities and near work, both at baseline and at follow-up visits.ResultsA total of 1793 subjects were recruited, of whom 709 children comprised the COVID-19 cohort with 7.89±2.30 months of follow-up, and 1084 children comprised the pre-COVID-19 cohort with 37.54±3.12 months of follow-up. The overall incidence was 19.44% in the COVID-19 cohort, and 36.57% in pre-COVID-19 cohort. During the COVID-19 pandemic, the change in SER and axial length was –0.50±0.51 D and 0.29±0.35 mm, respectively; the time spent on outdoor activities decreased from 1.27±1.12 to 0.41±0.90 hours/day (p<0.001), while screen time increased from 2.45±2.32 to 6.89±4.42 hours/day (p<0.001).ConclusionsWe showed a potential increase in myopia incidence, significant decrease in outdoor time and increase in screen time among schoolchildren in Hong Kong during the COVID-19 pandemic. Our results serve to warn eye care professionals, and also policy makers, educators and parents, that collective efforts are needed to prevent childhood myopia—a potential public health crisis as a result of COVID-19.

Danshen (salvia miltiorrhiza Bunge) is widely used in traditional Chinese medicine. However, it is definite clinical effort and mechanism on breast cancer is unclear. In our study, we used the real-world database to investigate in vivo protective effort of danshen in the breast cancer patients through using population-based data from the Taiwan National Health Insurance Research Database (NHIRD). In vitro , human breast cancer cells (MCF-7 cells and MDA-MB-231 cells) were used to investigate the effect and the underlying mechanism through XTT assay, flow cytometry, glutathione peroxidase (GPX) activity assay, GSH (reduced glutathione)/GSSG (oxidized glutathione), malondialdehyde (MDA), and western blot analysis. The in vivo effect was investigated through a xenograft nude mouse model. We found that dihydroisotanshinone I (DT), a pure compound present in danshen, can inhibit the growth of breast carcinoma cells, including MCF-7 cells and MDA-MB-231 cells. Moreover, DT induced apoptosis and ferroptosis in these breast cancer cells. DT also repressed the protein expression of GPX4 (Glutathione peroxidase 4). For in vivo study, DT treatment also significantly inhibited the final tumor volume without adverse effects in a xenograft nude mouse model. In conclusion, danshen has protective efforts in breast cancer patients, which could be attributed to DT through inducing apoptosis and ferroptosis of breast cancer cells.

The vascularity of the choroid has been implicated in the pathogenesis of various eye diseases. To date, no established quantifiable parameters to estimate vascular status of the choroid exists. Choroidal vascularity index (CVI) may potentially be used to assess vascular status of the choroid. We aimed to establish normative database for CVI and identify factors associated with CVI in healthy eyes. In this population-based study on 345 healthy eyes, choroidal enhanced depth imaging optical coherence tomography scans were segmented by modified image binarization technique. Total subfoveal choroidal area (TCA) was segmented into luminal (LA) and stromal (SA) area. CVI was calculated as the proportion of LA to TCA. Linear regression was used to identify ocular and systemic factors associated with CVI and subfoveal choroidal thickness (SFCT). Subfoveal CVI ranged from 60.07 to 71.27% with a mean value of 65.61 ± 2.33%. CVI was less variable than SFCT (coefficient of variation for CVI was 3.55 vs 40.30 for SFCT). Higher CVI was associated with thicker SFCT, but not associated with most physiological variables. CVI was elucidated as a significant determinant of SFCT. While SFCT was affected by many factors, CVI remained unaffected suggesting CVI to be a more robust marker of choroidal diseases.

Diabetic kidney disease (DKD) and diabetic retinopathy (DR) are major diabetic microvascular complications, contributing significantly to morbidity, disability, and mortality worldwide. The kidney and the eye, having similar microvascular structures and physiological and pathogenic features, may experience similar metabolic changes in diabetes. This study aimed to use machine learning (ML) methods integrated with metabolic data to identify biomarkers associated with DKD and DR in a multiethnic Asian population with diabetes, as well as to improve the performance of DKD and DR detection models beyond traditional risk factors. We used ML algorithms (logistic regression [LR] with Least Absolute Shrinkage and Selection Operator and gradient-boosting decision tree) to analyze 2772 adults with diabetes from the Singapore Epidemiology of Eye Diseases study, a population-based cross-sectional study conducted in Singapore (2004-2011). From 220 circulating metabolites and 19 risk factors, we selected the most important variables associated with DKD (defined as an estimated glomerular filtration rate <60 mL/min/1.73 m ) and DR (defined as an Early Treatment Diabetic Retinopathy Study severity level ≥20). DKD and DR detection models were developed based on the variable selection results and externally validated on a sample of 5843 participants with diabetes from the UK biobank (2007-2010). Machine-learned model performance (area under the receiver operating characteristic curve [AUC] with 95% CI, sensitivity, and specificity) was compared to that of traditional LR adjusted for age, sex, diabetes duration, hemoglobin A , systolic blood pressure, and BMI. Singapore Epidemiology of Eye Diseases participants had a median age of 61.7 (IQR 53.5-69.4) years, with 49.1% (1361/2772) being women, 20.2% (555/2753) having DKD, and 25.4% (685/2693) having DR. UK biobank participants had a median age of 61.0 (IQR 55.0-65.0) years, with 35.8% (2090/5843) being women, 6.7% (374/5570) having DKD, and 6.1% (355/5843) having DR. The ML algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with cardiovascular disease history, antihypertensive medication use, and 3 metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density lipoprotein), while DR was additionally associated with hemoglobin A , blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional LR models in both internal (AUC 0.838 vs 0.743 for DKD and 0.790 vs 0.764 for DR) and external validation (AUC 0.791 vs 0.691 for DKD and 0.778 vs 0.760 for DR). This study highlighted diabetes duration, insulin usage, age, and circulating tyrosine as important factors in detecting DKD and DR. The integration of ML with biomedical big data enables biomarker discovery and improves disease detection beyond traditional risk factors.

Background/aimsTo evaluate the performances of deep learning (DL) algorithms for detection of presence and extent pterygium, based on colour anterior segment photographs (ASPs) taken from slit-lamp and hand-held cameras.MethodsReferable pterygium was defined as having extension towards the cornea from the limbus of >2.50 mm or base width at the limbus of >5.00 mm. 2503 images from the Singapore Epidemiology of Eye Diseases (SEED) study were used as the development set. Algorithms were validated on an internal set from the SEED cohort (629 images (55.3% pterygium, 8.4% referable pterygium)), and tested on two external clinic-based sets (set 1 with 2610 images (2.8% pterygium, 0.7% referable pterygium, from slit-lamp ASP); and set 2 with 3701 images, 2.5% pterygium, 0.9% referable pterygium, from hand-held ASP).ResultsThe algorithm’s area under the receiver operating characteristic curve (AUROC) for detection of any pterygium was 99.5%(sensitivity=98.6%; specificity=99.0%) in internal test set, 99.1% (sensitivity=95.9%, specificity=98.5%) in external test set 1 and 99.7% (sensitivity=100.0%; specificity=88.3%) in external test set 2. For referable pterygium, the algorithm’s AUROC was 98.5% (sensitivity=94.0%; specificity=95.3%) in internal test set, 99.7% (sensitivity=87.2%; specificity=99.4%) in external set 1 and 99.0% (sensitivity=94.3%; specificity=98.0%) in external set 2.ConclusionDL algorithms based on ASPs can detect presence of and referable-level pterygium with optimal sensitivity and specificity. These algorithms, particularly if used with a handheld camera, may potentially be used as a simple screening tool for detection of referable pterygium. Further validation in community setting is warranted.Synopsis/precisDL algorithms based on ASPs can detect presence of and referable-level pterygium optimally, and may be used as a simple screening tool for the detection of referable pterygium in community screenings.

According to a 2013–2014 survey of nearly 46,000 U.S. adults and youths, 28% of adults were current users of tobacco and 9% of youths had used tobacco in the previous 30 days. Approximately 40% of users used multiple products, with cigarettes plus e-cigarettes the most common combination. Smoking is responsible for more U.S. deaths annually than the acquired immunodeficiency syndrome, use of alcohol and illegal drugs, motor vehicle accidents, murders, and suicides combined. 1 With recent data suggesting higher smoking-attributable mortality than previously estimated, 2 the medical community is urged to make tobacco control a high priority. 3 The prevalence of current use of cigarettes has declined during the past 50 years, from 42% of adults in 1965 to less than 20% in 2014, 4 , 5 but disparities in cigarette smoking across demographic subgroups (particularly according to race or ethnic group, educational attainment, and socioeconomic status) have widened during the past . . .

To examine prevalence and risk factors of epiretinal membrane (ERM) in a large, contemporary, multiethnic Asian population. Combined analysis of three population-based studies of eye diseases, with a total of 9799 Chinese, Malays and Indians residing in the general communities of Singapore. A comprehensive ophthalmic examination, interviews and laboratory blood tests were performed to assess potential risk factors. Digital retinal photographs were used to assess ERM according to a standardised protocol. ERM was classified into cellophane macular reflex (CMR) and/or preretinal macular fibrosis (PMF), and also as primary or secondary (in eyes with other retinal pathology or a history of cataract surgery). The age-standardised and ethnicity-standardised prevalence was 12.1% for any ERM, 6.8% for CMR, 6.7% for PMF and 2.8% for bilateral ERM. ERM prevalence was higher in Chinese (13.0%) compared with Malays (7.9%) or Indians (8.7%). In multivariate analysis, significant factors associated with primary ERM were older age (OR 1.08 per year increase; p<0.01), Chinese ethnicity (OR 1.60 vs Indians; p<0.01; OR 1.39 vs Malays; p<0.01), smoking (OR 0.70; p=0.01), longer axial length (OR 1.07 per mm increase; p=0.03) and cataract (OR 0.64; p<0.01). Significant factors independently associated with secondary ERM were older age (OR 1.05; p<0.01), cataract surgery (OR 10.6; p<0.01) and diabetic retinopathy (OR 2.48; p<0.01). ERM is common in Asians, particularly among Chinese. Older age is the most consistent risk factor for any ERM, and previous cataract surgery and diabetic retinopathy are the strongest risk factors for secondary ERM.

COVID-19 has led to massive disruptions in societal, economic and healthcare systems globally. While COVID-19 has sparked a surge and expansion of new digital business models in different industries, healthcare has been slower to adapt to digital solutions. The majority of ophthalmology clinical practices are still operating through a traditional model of ‘brick-and-mortar’ facilities and ‘face-to-face’ patient–physician interaction. In the current climate of COVID-19, there is a need to fuel implementation of digital health models for ophthalmology. In this article, we highlight the current limitations in traditional clinical models as we confront COVID-19, review the current lack of digital initiatives in ophthalmology sphere despite the presence of COVID-19, propose new digital models of care for ophthalmology and discuss potential barriers that need to be considered for sustainable transformation to take place.

Perinatal stress, anxiety, and depression impacts not only women but also their child(ren). The purpose of this longitudinal study is to explore trends of stress, anxiety, and depressive symptoms from pregnancy to postpartum and understand predictions of stress and anxiety on postpartum depression. One-hundred-fifty-six women at 23–28 weeks gestation (T1), 147 at 32–36 weeks gestation (T2), 129 at over 36 weeks gestation (T3), and 83 at postpartum (T4) completed study surveys. The Perceived Stress Scale, Center for Epidemiologic Studies Depression scale, and State-Trait Anxiety Inventory were used to measure stress, depressive symptoms, and anxiety. Descriptive statistics, Pearson and Spearman’s correlation, and Generalized Estimating Equation were applied to analyze the data. Results showed that levels of anxiety and depressive symptoms increased from 24 weeks gestation to postpartum, whereas stress levels decreased during pregnancy but increased in postpartum. Over half of women experienced anxiety symptoms, especially during late pregnancy and postpartum. Stress, anxiety, and depressive symptoms were inter-correlated. Notably, women at late pregnancy and postpartum were prone to stress, anxiety, and depression. Prenatal anxiety could predict postpartum depressive symptoms. Active assessment and management of stress, anxiety, and depression is needed and should begin from early pregnancy and continue until postpartum.