Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases.

OBJECTIVE: To review recent technological advancement in imaging, surgical visualization, robotics technology, and the use of artificial intelligence in surgical vitreoretinal (VR) diseases. BACKGROUND: Technological advancements in imaging enhance both preoperative and intraoperative management of surgical VR diseases. Widefield imaging in fundal photography and OCT can improve assessment of peripheral retinal disorders such as retinal detachments, degeneration, and tumors. OCT angiography provides a rapid and noninvasive imaging of the retinal and choroidal vasculature. Surgical visualization has also improved with intraoperative OCT providing a detailed real-time assessment of retinal layers to guide surgical decisions. Heads-up display and head-mounted display utilize 3-dimensional technology to provide surgeons with enhanced visual guidance and improved ergonomics during surgery. Intraocular robotics technology allows for greater surgical precision and is shown to be useful in retinal vein cannulation and subretinal drug delivery. In addition, deep learning techniques leverage on diverse data including widefield retinal photography and OCT for better predictive accuracy in classification, segmentation, and prognostication of many surgical VR diseases. CONCLUSION: This review article summarized the latest updates in these areas and highlights the importance of continuous innovation and improvement in technology within the field. These advancements have the potential to reshape management of surgical VR diseases in the very near future and to ultimately improve patient care. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Topical Atropine for Childhood Myopia Control: The Atropine Treatment Long-Term Assessment Study.

Importance: Clinical trial results of topical atropine eye drops for childhood myopia control have shown inconsistent outcomes across short-term studies, with little long-term safety or other outcomes reported. Objective: To report the long-term safety and outcomes of topical atropine for childhood myopia control. Design, Setting, and Participants: This prospective, double-masked observational study of the Atropine for the Treatment of Myopia (ATOM) 1 and ATOM2 randomized clinical trials took place at 2 single centers and included adults reviewed in 2021 through 2022 from the ATOM1 study (atropine 1% vs placebo; 1999 through 2003) and the ATOM2 study (atropine 0.01% vs 0.1% vs 0.5%; 2006 through 2012). Main Outcome Measures: Change in cycloplegic spherical equivalent (SE) with axial length (AL); incidence of ocular complications. Results: Among the original 400 participants in each original cohort, the study team evaluated 71 of 400 ATOM1 adult participants (17.8% of original cohort; study age, mean [SD] 30.5 [1.2] years; 40.6% female) and 158 of 400 ATOM2 adult participants (39.5% of original cohort; study age, mean [SD], 24.5 [1.5] years; 42.9% female) whose baseline characteristics (SE and AL) were representative of the original cohort. In this study, evaluating ATOM1 participants, the mean (SD) SE and AL were -5.20 (2.46) diopters (D), 25.87 (1.23) mm and -6.00 (1.63) D, 25.90 (1.21) mm in the 1% atropine-treated and placebo groups, respectively (difference of SE, 0.80 D; 95% CI, -0.25 to 1.85 D; P = .13; difference of AL, -0.03 mm; 95% CI, -0.65 to 0.58 mm; P = .92). In ATOM2 participants, the mean (SD) SE and AL was -6.40 (2.21) D; 26.25 (1.34) mm; -6.81 (1.92) D, 26.28 (0.99) mm; and -7.19 (2.87) D, 26.31 (1.31) mm in the 0.01%, 0.1%, and 0.5% atropine groups, respectively. There was no difference in the 20-year incidence of cataract/lens opacities, myopic macular degeneration, or parapapillary atrophy (ß/γ zone) comparing the 1% atropine-treated group vs the placebo group. Conclusions and Relevance: Among approximately one-quarter of the original participants, use of short-term topical atropine eye drops ranging from 0.01% to 1.0% for a duration of 2 to 4 years during childhood was not associated with differences in final refractive errors 10 to 20 years after treatment. There was no increased incidence of treatment or myopia-related ocular complications in the 1% atropine-treated group vs the placebo group. These findings may affect the design of future clinical trials, as further studies are required to investigate the duration and concentration of atropine for childhood myopia control.

Artificial intelligence and digital solutions for myopia.

Myopia as an uncorrected visual impairment is recognized as a global public health issue with an increasing burden on health-care systems. Moreover, high myopia increases one's risk of developing pathologic myopia, which can lead to irreversible visual impairment. Thus, increased resources are needed for the early identification of complications, timely intervention to prevent myopia progression, and treatment of complications. Emerging artificial intelligence (AI) and digital technologies may have the potential to tackle these unmet needs through automated detection for screening and risk stratification, individualized prediction, and prognostication of myopia progression. AI applications in myopia for children and adults have been developed for the detection, diagnosis, and prediction of progression. Novel AI technologies, including multimodal AI, explainable AI, federated learning, automated machine learning, and blockchain, may further improve prediction performance, safety, accessibility, and also circumvent concerns of explainability. Digital technology advancements include digital therapeutics, self-monitoring devices, virtual reality or augmented reality technology, and wearable devices - which provide possible avenues for monitoring myopia progression and control. However, there are challenges in the implementation of these technologies, which include requirements for specific infrastructure and resources, demonstrating clinically acceptable performance and safety of data management. Nonetheless, this remains an evolving field with the potential to address the growing global burden of myopia.

Application of artificial intelligence in cataract management: current and future directions.

The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract  is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. This utilizes a deep-learning, convolutional neural network (CNN) to detect and classify referable cataracts appropriately. Second, some of the latest intraocular lens formulas have used AI to enhance prediction accuracy, achieving superior postoperative refractive results compared to traditional formulas. Third, AI can be used to augment cataract surgical skill training by identifying different phases of cataract surgery on video and to optimize operating theater workflows by accurately predicting the duration of surgical procedures. Fourth, some AI CNN models are able to effectively predict the progression of posterior capsule opacification and eventual need for YAG laser capsulotomy. These advances in AI could transform cataract management and enable delivery of efficient ophthalmic services. The key challenges include ethical management of data, ensuring data security and privacy, demonstrating clinically acceptable performance, improving the generalizability of AI models across heterogeneous populations, and improving the trust of end-users.

Digital Technology for AMD Management in the Post-COVID-19 New Normal.

PURPOSE: The COVID-19 pandemic has put strain on healthcare systems and the availability and allocation of healthcare manpower, resources and infrastructure. With immediate priorities to protect the health and safety of both patients and healthcare service providers, ophthalmologists globally were advised to defer nonurgent cases, while at the same time managing sight-threatening conditions such as neovascular Age-related Macular Degeneration (AMD). The management of AMD patients both from a monitoring and treatment perspective presents a particular challenge for ophthalmologists. This review looks at how these pressures have encouraged the acceptance and speed of adoption of digitalization. DESIGN AND METHODS: A literature review was conducted on the use of digital technology during COVID-19 pandemic, and on the transformation of medicine, ophthalmology and AMD screening through digitalization. RESULTS: In the management of AMD, the implementation of artificial intelligence and "virtual clinics" have provided assistance in screening, diagnosis, monitoring of the progression and the treatment of AMD. In addition, hardware and software developments in home monitoring devices has assisted in self-monitoring approaches. CONCLUSIONS: Digitalization strategies and developments are currently ongoing and underway to ensure early detection, stability and visual improvement in patients suffering from AMD in this COVID-19 era. This may set a precedence for the post COVID-19 new normal where digital platforms may be routine, standard and expected in healthcare delivery.

Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.

BACKGROUND: Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings of two deep learning approaches as compared with the current human assessment: a semi-automated deep learning model as a triage filter before secondary human assessment; and a fully automated deep learning model without human assessment. METHODS: In this economic analysis modelling study, using 39 006 consecutive patients with diabetes in a national diabetic retinopathy screening programme in Singapore in 2015, we used a decision tree model and TreeAge Pro to compare the actual cost of screening this cohort with human graders against the simulated cost for semi-automated and fully automated screening models. Model parameters included diabetic retinopathy prevalence rates, diabetic retinopathy screening costs under each screening model, cost of medical consultation, and diagnostic performance (ie, sensitivity and specificity). The primary outcome was total cost for each screening model. Deterministic sensitivity analyses were done to gauge the sensitivity of the results to key model assumptions. FINDINGS: From the health system perspective, the semi-automated screening model was the least expensive of the three models, at US$62 per patient per year. The fully automated model was $66 per patient per year, and the human assessment model was $77 per patient per year. The savings to the Singapore health system associated with switching to the semi-automated model are estimated to be $489 000, which is roughly 20% of the current annual screening cost. By 2050, Singapore is projected to have 1 million people with diabetes; at this time, the estimated annual savings would be $15 million. INTERPRETATION: This study provides a strong economic rationale for using deep learning systems as an assistive tool to screen for diabetic retinopathy. FUNDING: Ministry of Health, Singapore.

Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review.

BACKGROUND: Effective screening is a desirable method for the early detection and successful treatment for diabetic retinopathy, and fundus photography is currently the dominant medium for retinal imaging due to its convenience and accessibility. Manual screening using fundus photographs has however involved considerable costs for patients, clinicians and national health systems, which has limited its application particularly in less-developed countries. The advent of artificial intelligence, and in particular deep learning techniques, has however raised the possibility of widespread automated screening. MAIN TEXT: In this review, we first briefly survey major published advances in retinal analysis using artificial intelligence. We take care to separately describe standard multiple-field fundus photography, and the newer modalities of ultra-wide field photography and smartphone-based photography. Finally, we consider several machine learning concepts that have been particularly relevant to the domain and illustrate their usage with extant works. CONCLUSIONS: In the ophthalmology field, it was demonstrated that deep learning tools for diabetic retinopathy show clinically acceptable diagnostic performance when using colour retinal fundus images. Artificial intelligence models are among the most promising solutions to tackle the burden of diabetic retinopathy management in a comprehensive manner. However, future research is crucial to assess the potential clinical deployment, evaluate the cost-effectiveness of different DL systems in clinical practice and improve clinical acceptance.

Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy.

Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings. Using 455,491 retinal images, we evaluated two technical and three image-related factors in detection of referable DR. For technical factors, the performances of four DL models (VGGNet, ResNet, DenseNet, Ensemble) and two computational frameworks (Caffe, TensorFlow) were evaluated while for image-related factors, we evaluated image compression levels (reducing image size, 350, 300, 250, 200, 150 KB), number of fields (7-field, 2-field, 1-field) and media clarity (pseudophakic vs phakic). In detection of referable DR, four DL models showed comparable diagnostic performance (AUC 0.936-0.944). To develop the VGGNet model, two computational frameworks had similar AUC (0.936). The DL performance dropped when image size decreased below 250 KB (AUC 0.936, 0.900, p < 0.001). The DL performance performed better when there were increased number of fields (dataset 1: 2-field vs 1-field-AUC 0.936 vs 0.908, p < 0.001; dataset 2: 7-field vs 2-field vs 1-field, AUC 0.949 vs 0.911 vs 0.895). DL performed better in the pseudophakic than phakic eyes (AUC 0.918 vs 0.833, p < 0.001). Various image-related factors play more significant roles than technical factors in determining the diagnostic performance, suggesting the importance of having robust training and testing datasets for DL training and deployment in the real-world settings.

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.

BACKGROUND: Radical measures are required to identify and reduce blindness due to diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of an artificial intelligence (AI) model using deep learning in a population-based diabetic retinopathy screening programme in Zambia, a lower-middle-income country. METHODS: We adopted an ensemble AI model consisting of a combination of two convolutional neural networks (an adapted VGGNet architecture and a residual neural network architecture) for classifying retinal colour fundus images. We trained our model on 76 370 retinal fundus images from 13 099 patients with diabetes who had participated in the Singapore Integrated Diabetic Retinopathy Program, between 2010 and 2013, which has been published previously. In this clinical validation study, we included all patients with a diagnosis of diabetes that attended a mobile screening unit in five urban centres in the Copperbelt province of Zambia from Feb 1 to June 31, 2012. In our model, referable diabetic retinopathy was defined as moderate non-proliferative diabetic retinopathy or worse, diabetic macular oedema, and ungradable images. Vision-threatening diabetic retinopathy comprised severe non-proliferative and proliferative diabetic retinopathy. We calculated the area under the curve (AUC), sensitivity, and specificity for referable diabetic retinopathy, and sensitivities of vision-threatening diabetic retinopathy and diabetic macular oedema compared with the grading by retinal specialists. We did a multivariate analysis for systemic risk factors and referable diabetic retinopathy between AI and human graders. FINDINGS: A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited. Referable diabetic retinopathy was found in 697 (22·5%) eyes, vision-threatening diabetic retinopathy in 171 (5·5%) eyes, and diabetic macular oedema in 249 (8·1%) eyes. The AUC of the AI system for referable diabetic retinopathy was 0·973 (95% CI 0·969-0·978), with corresponding sensitivity of 92·25% (90·10-94·12) and specificity of 89·04% (87·85-90·28). Vision-threatening diabetic retinopathy sensitivity was 99·42% (99·15-99·68) and diabetic macular oedema sensitivity was 97·19% (96·61-97·77). The AI model and human graders showed similar outcomes in referable diabetic retinopathy prevalence detection and systemic risk factors associations. Both the AI model and human graders identified longer duration of diabetes, higher level of glycated haemoglobin, and increased systolic blood pressure as risk factors associated with referable diabetic retinopathy. INTERPRETATION: An AI system shows clinically acceptable performance in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, and diabetic macular oedema in population-based diabetic retinopathy screening. This shows the potential application and adoption of such AI technology in an under-resourced African population to reduce the incidence of preventable blindness, even when the model is trained in a different population. FUNDING: National Medical Research Council Health Service Research Grant, Large Collaborative Grant, Ministry of Health, Singapore; the SingHealth Foundation; and the Tanoto Foundation.

Five Community-wide Approaches to Physical Activity Promotion: A Cluster Analysis of These Activities in Local Health Jurisdictions in 6 States.

OBJECTIVE: Local health departments (LHDs) have essential roles to play in ensuring the promotion of physical activity (PA) in their communities in order to reduce obesity. Little research exists, however, regarding the existence of these PA interventions across communities and how these interventions may impact community health. DESIGN: In this exploratory study, we used cluster analysis to identify the structure of co-occurring PA interventions, followed by regression analysis to quantify the association between the patterns of PA interventions and prevalence of PA and obesity at a population level. SETTING: Our study setting included local health jurisdictions in Colorado, Florida, Minnesota, New Jersey, Tennessee, and Washington. PARTICIPANTS: Participating jurisdictions were those 218 local health jurisdictions (mostly counties) from which LHD leaders had provided data in 2013 for the Multi-Network Practice and Outcome Variation Examination Study. MAIN OUTCOME MEASURES: We obtained unique public health activities data on PA interventions conducted in 2012 from 218 LHDs in 6 participating states. We categorized jurisdictions using cluster analysis, based on PA intervention approaches indicated by LHD leaders as available in their communities and then examined associations between categories and prevalence of obesity and of residents engaged in PA. RESULTS: We identified 5 distinct PA intervention categories representing community-wide approaches-Comprehensive Approach, Built Environment, Personal Health, School-Based Interventions, and No Apparent Activities. Prevalence rates of obesity and PA among jurisdictions in the intervention clusters were significantly different from jurisdictions with No Apparent Activities, with more population-level approaches most significantly related to beneficial outcomes. CONCLUSION: Our findings suggest the importance of standardized public health services data for generating evidence regarding health-related outcomes. The intervention categories we identified appear to reflect broad, local community-wide prevention approaches and demonstrated that population-level PA interventions can be testable and may have particularly beneficial relationships to community health. Widespread adoption of such standardized data depicting local public health prevention activity could support monitoring practice change, performance improvement, comparisons across communities that could reduce unnecessary variation, and the generation of evidence for public health practice and policy-making.

Developing the Evidence for Public Health Systems to Battle Vaccine Preventable Disease at the Local Level: Data Challenges and Strategies for Advancing Research.

CONTEXT: Generally decreasing local health department (LHD) resources devoted to immunization programs and changes in LHD roles in immunization services represent major shifts in a core LHD service. OBJECTIVE: Within a rapidly changing immunization landscape and emerging vaccine preventable disease outbreaks, our objective was to examine how LHD immunization expenditures are related to county-level immunization coverage and pertussis rates. DESIGN: We used a practice-based approach in which we collaborated with practice partners and uniquely detailed LHD immunization expenditure data. Our analyses modeled the ecologic relationship between LHD immunization expenditures and LHD system performance and health outcomes. SETTING: This study was launched through a consortium of public health Practice-Based Research Network states as part of a suite of studies examining the relationship between various LHD service-related expenditures and health outcomes. PARTICIPANTS: We investigated and sought to include all LHDs in the states of Florida, New York (except New York City's LHD), and Washington. OUTCOME MEASURES: With LHD immunization expenditures as our independent variable, our outcomes were 1 year of jurisdiction-level rates of toddler immunization completeness, to measure immunization system performance, and 11 years of annual jurisdiction-level numbers of pertussis cases per 100 000 population, to measure related health outcomes. RESULTS: Immunization completeness and pertussis rates varied greatly, but our models did not produce significant results despite numerous analytic approaches and while controlling for other factors. CONCLUSION: While our study was part of a suite of studies using similar methods and producing significant results, this study was instead challenged by serious data limitations and highlighted the gap in consistent, standardized data that can support critically needed evidence regarding immunization rates and disease. With LHDs at the epicenter of reducing vaccine preventable disease, it is vital to utilize emerging opportunities to understand the nature of their efforts in immunization coverage and disease prevention.

Local health department food safety and sanitation expenditures and reductions in enteric disease, 2000-2010.

OBJECTIVES: In collaboration with Public Health Practice-Based Research Networks, we investigated relationships between local health department (LHD) food safety and sanitation expenditures and reported enteric disease rates. METHODS: We combined annual infection rates for the common notifiable enteric diseases with uniquely detailed, LHD-level food safety and sanitation annual expenditure data obtained from Washington and New York state health departments. We used a multivariate panel time-series design to examine ecologic relationships between 2000-2010 local food safety and sanitation expenditures and enteric diseases. Our study population consisted of 72 LHDs (mostly serving county-level jurisdictions) in Washington and New York. RESULTS: While controlling for other factors, we found significant associations between higher LHD food and sanitation spending and a lower incidence of salmonellosis in Washington and a lower incidence of cryptosporidiosis in New York. CONCLUSIONS: Local public health expenditures on food and sanitation services are important because of their association with certain health indicators. Our study supports the need for program-specific LHD service-related data to measure the cost, performance, and outcomes of prevention efforts to inform practice and policymaking.

Regret triggers inaction inertia--but which regret and how?

When people miss a good bargain, they are less likely to take a subsequent one that is not as good. This phenomenon is termed inaction inertia. Two regret-based explanations of it have been proposed. According to one, people anticipate that buying the item will lead to regret because it will remind them that they missed a better opportunity to buy it. According to the other, the regret people experience when missing a bargain, together with a subjective devaluation of the item resulting from that, produces inaction inertia. In two studies, we assessed experienced regret, anticipated regret, subjective valuation (SV) of the bargain and likelihood of purchase. Our findings provide grounds for reconciling the above accounts. The former is more appropriate when the difference between the previous and subsequent bargain is large, and the latter is more appropriate when it is smaller. Furthermore, our findings suggest that previous accounts of inaction inertia are incomplete. Whereas subjective value and regret considerations jointly determine purchase likelihood when no previous opportunity has been missed, regret considerations are the sole determinant of this likelihood when such an opportunity has been missed. Inaction inertia arises at least partly because considering regret turns attention away from the financial advantages of taking the bargain.