Digital technology and COVID-19 Ting, Daniel Shu Wei; Carin, Lawrence; Dzau, Victor ...
Nature medicine,
04/2020, Letnik:
26, Številka:
4
Journal Article
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.
With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine ...learning and deep learning, is paving the way for ‘intelligent’ healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions. In this review, we provide an overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour, and highlight important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions.
Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural ...language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.
To review the impact of increased digital device usage arising from lockdown measures instituted during the COVID-19 pandemic on myopia and to make recommendations for mitigating potential ...detrimental effects on myopia control.
Perspective.
We reviewed studies focused on digital device usage, near work, and outdoor time in relation to myopia onset and progression. Public health policies on myopia control, recommendations on screen time, and information pertaining to the impact of COVID-19 on increased digital device use were presented. Recommendations to minimize the impact of the pandemic on myopia onset and progression in children were made.
Increased digital screen time, near work, and limited outdoor activities were found to be associated with the onset and progression of myopia, and could potentially be aggravated during and beyond the COVID-19 pandemic outbreak period. While school closures may be short-lived, increased access to, adoption of, and dependence on digital devices could have a long-term negative impact on childhood development. Raising awareness among parents, children, and government agencies is key to mitigating myopigenic behaviors that may become entrenched during this period.
While it is important to adopt critical measures to slow or halt the spread of COVID-19, close collaboration between parents, schools, and ministries is necessary to assess and mitigate the long-term collateral impact of COVID-19 on myopia control policies.
SARS emerged in 2003 and caused the first pandemic of the 21st century, affecting more than 8000 people, killing 774 in 26 countries.5 MERS coronavirus (MERS-CoV) was isolated in 20126 and has seen ...over 2400 cases reported to WHO to date, and over 850 deaths.7 Of the 2223 laboratory-confirmed MERS-CoV cases reported to WHO, 415 were healthcare workers, representing over one-third of all secondary transmission.8 SARS and MERS spread principally by direct transmission and respiratory droplets.9 10 However, SARS-CoV, and perhaps also MERS-CoV, may shed and be transferred to environmental surfaces, and thence contaminate hands and mucous membranes subsequently.10 Ocular involvement has not been described with either MERS-CoV or SARS-CoV11–13 although polymerase chain reaction on tears from patients with SARS-CoV infection demonstrated presence of the virus.14 There is also evidence that some coronavirus can occasionally cause conjunctivitis in humans. ...human coronavirus NL 63 (HCoV-NL63) was first identified in a baby with bronchiolitis and conjunctivitis.15 Subsequent in 28 cases of children with confirmed HCoV-NL63 infections, 17% had conjunctivitis.16 There is now growing evidence that human-to-human transmission is occurring among close contacts, and reports that >1,700 healthcare professionals having been infected with 6 deaths including oneophthalmologist.17–19 Of the affected healthcare workers, one was part of the expert task force who visited Wuhan, and he has reflected on his experience of the disease. The usual measures that apply to prevention of infection transmission, in particular thorough handwashing, should apply. ...we learn more, it is prudent to avoid touching the eyes where possible in at risk locations, in particular for healthcare workers in the hospital environment. Healthcare professionals should take the full recommended measures including strict hand hygiene and protecting the exposed mucous membranes, including wearing goggles or face masks.25 It would be prudent to question patients directly if they had any symptoms and signs of conjunctivitis prior respiratory and other systemic symptoms to help further our understanding of the natural history of the disease.
Correspondence to Dr Daniel Shu Wei Ting, Vitreo-retinal Department, Singapore National Eye Center, Singapore 168751, Singapore; daniel.ting.s.w@singhealth.com.sg Artificial intelligence (AI) is the ...fourth industrial revolution.1 Deep learning is a robust machine learning technique that uses convolutional neural network to perform multilevel data abstraction without the need for manual feature engineering.2 In ophthalmology, many studies showed comparable, if not better, diagnostic performance in using AI to screen, diagnose, predict and monitor various eye conditions on fundus photographs and optical coherence tomography,3 4 including diabetic retinopathy (DR),5 age-related macular degeneration,6 glaucoma,7 retinopathy of prematurity (ROP).8 To date, many countries have reported well-established telemedicine programme to screen for DR and ROP,9–12 but limited for cataracts. The study showed that the AI algorithm had excellent diagnostic performance (area under the curve >90%) in identifying the correct capture modes (mydriatic vs non-mydriatic and diffuse vs lateral illumination), lens status (normal, cataractous or artificial lens) and the ‘referable’ cases (as defined above). ...the authors also described a telemedicine platform to enable home monitoring (using the ocular surface images taken by family members using cell phones, visual acuity and brief clinical history), followed by referral to the community-based healthcare facilities (where the anterior segment images were captured using a slit lamp microscope, in the telemedicine platform with AI analysis), and to the tertiary settings via a fast-tract notification system for those cases that were deemed referable. In addition to its screening and diagnostic values, AI technologies have been extended to other aspects of cataract and cataract surgery, including biometry for intraocular lens (IOL) power calculation,19 corneal power evaluation following laser refractive surgery20 and identification of phases in videos of cataract surgery for training purpose.21 For instance, Sramka et al 19 reported that machine learning, using support vector machine regression model and multilayer neural network ensemble model, could achieve significantly better results in IOL calculation compared with conventional clinical method, thereby optimising the refractive outcomes following cataract surgery.19 In the future, we envisage that AI technologies may be potentially applied in other cataract-related areas such as preoperative risk stratification for cataract surgery, particularly in predicting the risk of posterior capsular rupture, prediction of postoperative visual and refractive outcomes and patient assessment and selection for implants such as multifocal or accommodative IOLs. ...an AI-assisted telemedicine platform for cataract is useful for many large countries with limited access to the tertiary care health services, aiming to reduce cataract-related visual impairment.
To determine the incremental cost-effectiveness of a new telemedicine technician-based assessment relative to an existing model of family physician (FP)-based assessment of diabetic retinopathy (DR) ...in Singapore from the health system and societal perspectives.
Model-based, cost-effectiveness analysis of the Singapore Integrated Diabetic Retinopathy Program (SiDRP).
A hypothetical cohort of patients aged 55 years with type 2 diabetes previously not screened for DR.
The SiDRP is a new telemedicine-based DR screening program using trained technicians to assess retinal photographs. We compared the cost-effectiveness of SiDRP with the existing model in which FPs assess photographs. We developed a hybrid decision tree/Markov model to simulate the costs, effectiveness, and incremental cost-effectiveness ratio (ICER) of SiDRP relative to FP-based DR screening over a lifetime horizon. We estimated the costs from the health system and societal perspectives. Effectiveness was measured in terms of quality-adjusted life-years (QALYs). Result robustness was calculated using deterministic and probabilistic sensitivity analyses.
The ICER.
From the societal perspective that takes into account all costs and effects, the telemedicine-based DR screening model had significantly lower costs (total cost savings of S$173 per person) while generating similar QALYs compared with the physician-based model (i.e., 13.1 QALYs). From the health system perspective that includes only direct medical costs, the cost savings are S$144 per person. By extrapolating these data to approximately 170 000 patients with diabetes currently being screened yearly for DR in Singapore's primary care polyclinics, the present value of future cost savings associated with the telemedicine-based model is estimated to be S$29.4 million over a lifetime horizon.
While generating similar health outcomes, the telemedicine-based DR screening using technicians in the primary care setting saves costs for Singapore compared with the FP model. Our data provide a strong economic rationale to expand the telemedicine-based DR screening program in Singapore and elsewhere.
IMPORTANCE: Optical coherence tomographic angiography (OCT-A) is able to visualize retinal microvasculature without the need for injection of fluorescein contrast dye. Nevertheless, it is only able ...to capture a limited view of macula and does not show leakage. OBJECTIVES: To evaluate the retinal microvasculature using OCT-A in patients with type 2 diabetes as well as the association of OCT-A characteristics with diabetic retinopathy (DR) and systemic risk factors. DESIGN, SETTING, AND PARTICIPANTS: A prospective, observational study was conducted from January 1 to June 30, 2016, at medical retina clinics at the Singapore National Eye Center among 50 patients with type 2 diabetes with and without DR (n = 100 eyes). We examined the retinal microvasculature with swept-source OCT-A and a semiautomated software to measure the capillary density index (CDI) and fractal dimension (FD) at the superficial vascular plexus (SVP) and deep retinal vascular plexus (DVP). We collected data on histories of patients’ glycated hemoglobin A1c, hypertension, hyperlipidemia, smoking, and renal impairment. MAIN OUTCOMES AND MEASURES: The CDI and FD at the SVP and DVP for each severity level of DR and the association of systemic risk factors vs the CDI and FD. RESULTS: The mean (SD) glycated hemoglobin A1c of the 50 patients (26 men and 24 women; 35 Chinese; mean SD age, 59.5 8.9 years) was 7.9% (1.7%). The mean (SD) CDI at the SVP decreased from 0.358 (0.017) in patients with no DR to 0.338 (0.012) in patients with proliferative DR (P < .001) and at the DVP decreased in patients with no DR from 0.361 (0.019) to 0.345 (0.020) in patients with proliferative DR (P = .04). The mean (SD) FD at the SVP increased from 1.53 (0.05) in patients with no DR to 1.60 (0.05) in patients with proliferative DR (P < .01) and at the DVP increased from 1.55 (0.06) in patients with no DR to 1.61 (0.05) in patients with proliferative DR (P = .02). For systemic risk factors, hyperlipidemia (odds ratio OR, 9.82; 95% CI, 6.92-11.23; P < .001), smoking (OR, 10.90; 95% CI, 8.23-12.34; P < .001), and renal impairment (OR, 3.72; 95% CI, 1.80-4.81; P = .05) were associated with reduced CDI, while increased glycated hemoglobin A1c (≥8%) (OR, 8.77; 95% CI, 5.23-10.81; P < .01) and renal impairment (OR, 10.30; 95% CI, 8.21-11.91; P < .001) were associated with increased FD. CONCLUSIONS AND RELEVANCE: Optical coherence tomographic angiography is a novel imaging modality to quantify the retinal capillary microvasculature in patients with diabetes. It can be potentially used in interventional trials to study the effect of systemic risk factors on the microvasculature that was previously not accessible in a noninvasive manner. The relevance of these findings relative to visual acuity, however, remains largely unknown at this time.
Training the modern ophthalmic surgeon is a challenging process. Microsurgical education can benefit from innovative methods to practice surgery in low-risk simulations, assess and refine skills in ...the operating room through video content analytics, and learn at a distance from experienced surgeons. Developments in emerging technologies may allow us to pursue novel forms of instruction and build on current educational models. Artificial intelligence, which has already seen numerous applications in ophthalmology, may be used to facilitate surgical tracking and evaluation. Within immersive technology, growth in the space of virtual reality head-mounted displays has created intriguing possibilities for operating room simulation and observation. Here, we explore the applications of these technologies and comment on their future in ophthalmic surgical education.
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