The coronavirus disease 2019 (COVID-19) pandemic has overwhelmed healthcare services, faced with the twin challenges in acutely meeting the medical needs of patients with COVID-19 while continuing ...essential services for non-COVID-19 illnesses. The need to re-invent, re-organize and transform healthcare and co-ordinate clinical services at a population level is urgent as countries that controlled initial outbreaks start to experience resurgences. A wide range of digital health solutions have been proposed, although the extent of successful real-world applications of these technologies is unclear. This study aims to review applications of artificial intelligence (AI), telehealth, and other relevant digital health solutions for public health responses in the healthcare operating environment amidst the COVID-19 pandemic. A systematic scoping review was performed to identify potentially relevant reports. Key findings include a large body of evidence for various clinical and operational applications of telehealth (40.1%, n = 99/247). Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n = 111/247) and big data analytics (36.0%, n = 89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations. There were also few descriptions of applications for the internet of things (IoT) (2.0%, n = 5/247), digital platforms for communication (DC) (10.9%, 27/247), digital solutions for data management (DM) (1.6%, n = 4/247), and digital structural screening (DS) (8.9%, n = 22/247); representing gaps and opportunities for digital public health. Finally, the performance of digital health technology for operational applications related to population surveillance and points of entry have not been adequately evaluated.
Glaucoma is the leading cause of global irreversible blindness. Present estimates of global glaucoma prevalence are not up-to-date and focused mainly on European ancestry populations. We ...systematically examined the global prevalence of primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG), and projected the number of affected people in 2020 and 2040.
Systematic review and meta-analysis.
Data from 50 population-based studies (3770 POAG cases among 140,496 examined individuals and 786 PACG cases among 112 398 examined individuals).
We searched PubMed, Medline, and Web of Science for population-based studies of glaucoma prevalence published up to March 25, 2013. Hierarchical Bayesian approach was used to estimate the pooled glaucoma prevalence of the population aged 40-80 years along with 95% credible intervals (CrIs). Projections of glaucoma were estimated based on the United Nations World Population Prospects. Bayesian meta-regression models were performed to assess the association between the prevalence of POAG and the relevant factors.
Prevalence and projection numbers of glaucoma cases.
The global prevalence of glaucoma for population aged 40-80 years is 3.54% (95% CrI, 2.09-5.82). The prevalence of POAG is highest in Africa (4.20%; 95% CrI, 2.08-7.35), and the prevalence of PACG is highest in Asia (1.09%; 95% CrI, 0.43-2.32). In 2013, the number of people (aged 40-80 years) with glaucoma worldwide was estimated to be 64.3 million, increasing to 76.0 million in 2020 and 111.8 million in 2040. In the Bayesian meta-regression model, men were more likely to have POAG than women (odds ratio OR, 1.36; 95% CrI, 1.23-1.52), and after adjusting for age, gender, habitation type, response rate, and year of study, people of African ancestry were more likely to have POAG than people of European ancestry (OR, 2.80; 95% CrI, 1.83-4.06), and people living in urban areas were more likely to have POAG than those in rural areas (OR, 1.58; 95% CrI, 1.19-2.04).
The number of people with glaucoma worldwide will increase to 111.8 million in 2040, disproportionally affecting people residing in Asia and Africa. These estimates are important in guiding the designs of glaucoma screening, treatment, and related public health strategies.
Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, particularly for image classification and pattern ...recognition. In ophthalmology, applying DL for glaucoma assessment with optical coherence tomography (OCT), including OCT traditional reports, two-dimensional (2D) B-scans, and three-dimensional (3D) volumetric scans, has increasingly raised research interests. Studies have demonstrated that using DL for interpreting OCT is efficient, accurate, and with good performance for discriminating glaucomatous eyes from normal eyes, suggesting that incorporation of DL technology in OCT for glaucoma assessment could potentially address some gaps in the current practice and clinical workflow. However, further research is crucial in tackling some existing challenges, such as annotation standardization (i.e., setting a standard for ground truth labelling among different studies), development of DL-powered IT infrastructure for real-world implementation, prospective validation in unseen datasets for further evaluation of generalizability, cost-effectiveness analysis after integration of DL, the AI "black box" explanation problem. This review summarizes recent studies on the application of DL on OCT for glaucoma assessment, identifies the potential clinical impact arising from the development and deployment of the DL models, and discusses future research directions.
Glaucoma is a leading cause of global irreversible blindness. Primary open angle glaucoma (POAG) is the most common form of glaucoma and affects 44.1 million individuals worldwide. Elevation of ...intraocular pressure and impairment of vascular supply to the optic nerve head are two key pathogenic processes in the development of POAG. In this regard, chronic systemic conditions such as hypertension, diabetes and obesity have been postulated to be correlated with these two pathogenic processes. Thus, it is plausible that chronic systemic diseases may act as risk factors for POAG. The aim of this review is to summarize the current evidence on the associations of chronic systemic diseases with POAG. These information will help to further ascertain the risk factors for POAG and improve the early detection of POAG.
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.
To describe the pattern and risk factor traits associated with visual loss (defined as either visual impairment VI or blindness) in a multiethnic Asian cohort of Malay, Indian, and Chinese ...individuals living in Singapore.
A total of 10 020 participants from the Singapore Epidemiology of Eye Diseases Study were examined between 2004 and 2011. All underwent standardized examinations. VI (visual acuity <20/40 to ≥20/200) and blindness (visual acuity <20/200) were defined based on the US definition, better-seeing eye. Singapore Population Census 2010 was used to calculate age-standardized prevalence. Multiple logistic regression analysis was performed to determine the independent and joint risk factors associated with visual loss.
Malay individuals had higher age-standardized prevalence of best-corrected and presenting VI (5.4% and 19.9%, respectively) than Indian (3.6% and 18.0%) and Chinese individuals (3.3% and 17.2%). Cataract was the main cause for presenting and best-corrected blindness; cataract and diabetic retinopathy were the top causes for best-corrected VI, consistently observed across the 3 ethnic groups. Older age, female sex, lower socioeconomic status, diabetes, systemic comorbidities, and cognitive impairment were independently associated with increased risk of best-corrected visual loss (all P ≤ .027). Individuals aged ≥60 years with diabetes were 12.7 times (95% confidence interval, 8.39–19.23) likely to have best-corrected visual loss, compared with younger, nondiabetic individuals. Lower income and education explained 58.1% and 23.2% of best-corrected visual loss in this population, respectively.
In this urban multiethnic Asian population, we identified common traits associated with visual loss across Malay, Indian, and Chinese individuals. These results will be useful for the planning and designing of eye health services and strategies for Asia's rapidly developing populations living in urban communities. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.
Abstract
This study aimed to determine COVID-19-related awareness, knowledge, impact and preparedness among elderly Asians; and to evaluate their acceptance towards digital health services amidst the ...pandemic. 523 participants (177 Malays, 171 Indians, 175 Chinese) were recruited and underwent standardised phone interview during Singapore’s lockdown period (07 April till 01 June 2020). Multivariable logistic regression models were performed to evaluate the associations between demographic, socio-economic, lifestyle, and systemic factors, with COVID-19 awareness, knowledge, preparedness, well-being and digital health service acceptance. The average perception score on the seriousness of COVID-19 was 7.6 ± 2.4 (out of 10). 75.5% of participants were aware that COVID-19 carriers can be asymptomatic. Nearly all (≥ 90%) were aware of major prevention methods for COVID-19 (i.e. wearing of mask, social distancing). 66.2% felt prepared for the pandemic, and 86.8% felt confident with government’s handling and measures. 78.4% felt their daily routine was impacted. 98.1% reported no prior experience in using digital health services, but 52.2% felt these services would be helpful to reduce non-essential contact. 77.8% were uncomfortable with artificial intelligence software interpreting their medical results. In multivariable analyses, Chinese participants felt less prepared, and more likely felt impacted by COVID-19. Older and lower income participants were less likely to use digital health services. In conclusion, we observed a high level of awareness and knowledge on COVID-19. However, acceptance towards digital health service was low. These findings are valuable for examining the effectiveness of COVID-19 communication in Singapore, and the remaining gaps in digital health adoption among elderly.
PurposeTo evaluate glaucoma prevalence and disease burden across Asian subregions from 2013 to 2040.MethodsWe conducted a systematic review and meta-analysis of 23 population-based studies of 1318 ...primary open angle glaucoma (POAG) cases in 66 800 individuals and 691 primary angle closure glaucoma (PACG) cases in 72 767 individuals in Asia. Regions in Asia were defined based on United Nations’ (UN) classification of macro-geographic regions. PubMed, Medline and Web of Science databases were searched for population-based glaucoma prevalence studies using standardised criteria published to 31 December 2013. Pooled glaucoma prevalence for individuals aged 40–80 years was calculated using hierarchical Bayesian approaches. Prevalence differences by geographic subregion, subtype and habitation were examined with random effects meta-regression models. Estimates of individuals with glaucoma from 2013 to 2040 were based on the UN World Population Prospects.ResultsIn 2013, pooled overall glaucoma prevalence was 3.54% (95% credible interval (CrI) 1.83 to 6.28). POAG (2.34%, 95% CrI 0.96 to 4.55) predominated over PACG (0.73%, 95% CrI 0.18 to 1.96). With age and gender adjustment, PACG prevalence was higher in East than South East Asia (OR 5.55, 95% CrI 1.52 to 14.73), and POAG prevalence was higher in urban than rural populations (OR 2.11, 95% CrI 1.57 to 2.38). From 2013 to 2040, South Central Asia will record the steepest increase in number of glaucoma individuals from 17.06 million to 32.90 million compared with other Asian subregions. In 2040, South-Central Asia is also projected to overtake East Asia for highest overall glaucoma and POAG burden, while PACG burden remains highest in East Asia.ConclusionsAcross the Asian subregions, there was greater glaucoma burden in South-Central and East Asia. Sustainable public health strategies to combat glaucoma in Asia are needed.
Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of ...retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations between measurements of retinal-vessel calibre and CVD risk factors, including blood pressure, body-mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements performed by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of the features of retinal vessels in retinal photographs.Deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs perform comparably to or better than expert graders in associations of measurements of retinal-vessel calibre with cardiovascular risk factors.
PDF
