The diabetic retinopathy screening workflow : potential for smartphone imaging

Complications of diabetes mellitus, namely diabetic retinopathy and diabetic maculopathy, are the leading cause of blindness in working aged people. Sufferers can avoid blindness if identified early via retinal imaging. Systematic screening of the diabetic population has been shown to greatly reduce prevalence and incidence of blindness within the population. Many national screening programmes have digital fundus photography as their basis. In the past five years several techniques and adapters have been developed that allow digital fundus photography to be performed using smartphones. We review recent progress in smartphone - based fundus imaging and discuss its potential for integration into national systematic DR screening programmes. Some systems have produced promising initial results with respect to their agreement with reference standards. However further multi-site trialling of such systems’ use withi n implementable screening workflows is required if an evidence base strong enough to affect policy change is to be established. If this were to occur national diabetic retinopathy screening would, for the first time, become possible in low-and middle-income settings where cost and availability of trained eye-care personnel are currently key barriers to implementation. As diabetes prevalence and incidence is increasing sharply in these settings, the impact on global blindness could be profound

Towards a workflow driven design for mHealth devices within temporary eye clinics in low-income settings

Only a small minority of mobile healthcare technologies that have been successful in pilot studies have subsequently been integrated into healthcare systems. Understanding the reasons behind this discrepancy is crucial if such technologies are to be adopted. We believe that the mismatch is due to a breakdown in the relation between technical soundness of the original mobile health (mHealth) device design, and integration into healthcare provision workflows. Quantitative workflow modelling provides an opportunity to test this hypothesis. In this paper we present our current progress in developing a clinical workflow model for mobile eye assessment in low-income settings. We test the model for determining the appropriateness of design parameters of a mHealth device within this workflow, by assessing their impact on the entire clinical workflow performance

Improving equity, efficiency and adherence to referral in Pakistan's eye health programmes: Pre- and post-pandemic onset

BackgroundOver one billion people worldwide live with avoidable blindness or vision impairment. Eye Health Programmes tackle this by providing screening, primary eye care, refractive correction, and referral to hospital eye services. One point where patients can be lost in the treatment journey is adherence to hospital referral.ContextPeek Vision's software solutions have been used in Pakistan with the goal of increasing eye health programme coverage and effectiveness. This involved collaboration between health system stakeholders, international partners, local community leaders, social organizers and “Lady Health Workers”.ResultsFrom the beginning of the programmes in November 2018, to the end of December 2021, 393,759 people have been screened, 26% of whom (n = 101,236) needed refractive services or secondary eye care, and so were referred onwards to the triage centers or hospital services. Except for a short period affected heavily by COVID-19 pandemic, the programmes reached an increasing number of people over time: screening coverage improved from 774 people per month to over 28,300 people per month. Gathering and discussing data regularly with stakeholders and implementers has enabled continuous improvement to service delivery. The quality of screening and adherence to hospital visits, gender balance differences and waiting time to hospital visits were also improved. Overall attendance to hospital appointments improved in 2020 compared to 2019 from 45% (95% CI: 42–48%) to 78% (95% CI: 76–80%) in women, and from 48% (95% CI: 45–52%) to 70% (95% CI: 68–73%) in men. These patients also accessed treatment more quickly: 30-day hospital referral adherence improved from 12% in 2019 to 66% in 2020. This approach helped to utilize refractive services more efficiently, reducing false positive referrals to triage from 10.6 to 5.9%. Hospital-based services were also utilized more efficiently, as primary eye care services and refractive services were mainly delivered at the primary healthcare level.DiscussionDespite various challenges, we demonstrate how data-driven decisions can lead to health programme systems changes, including patient counseling and appointment reminders, which can effectively improve adherence to referral, allowing programmes to better meet their community's needs

A review of feature-based retinal image analysis

Retinal imaging is a fundamental tool in ophthalmic diagnostics. The potential use of retinal imaging within screening programs, with consequent need to analyze large numbers of images with high throughput, is pushing the digital image analysis field to find new solutions for the extraction of specific information from the retinal image. The aim of this review is to explore the latest progress in image processing techniques able to recognize specific retinal image features. and potential features of disease. In particular, this review aims to describe publically available retinal image databases, highlight different performance evaluators commonly used within the field, outline current approaches in feature-based retinal image analysis, and to map related trends. This review found two key areas to be addressed for the future development of automatic retinal image analysis: fundus image quality and the affect image processing may impose on relevant clinical information within the images. Performance evaluators of the algorithms reviewed are very promising, however absolute values are difficult to interpret when validating system suitability for use within clinical practice

Clinical validation of a smartphone-based adapter for optic disc imaging in Kenya

Visualization and interpretation of the optic nerve and retina are essential parts of most physical examinations. To design and validate a smartphone-based retinal adapter enabling image capture and remote grading of the retina. This validation study compared the grading of optic nerves from smartphone images with those of a digital retinal camera. Both image sets were independently graded at Moorfields Eye Hospital Reading Centre. Nested within the 6-year follow-up (January 7, 2013, to March 12, 2014) of the Nakuru Eye Disease Cohort in Kenya, 1460 adults (2920 eyes) 55 years and older were recruited consecutively from the study. A subset of 100 optic disc images from both methods were further used to validate a grading app for the optic nerves. Data analysis was performed April 7 to April 12, 2015. Vertical cup-disc ratio for each testwas compared in terms of agreement (Bland-Altman and weighted κ) and test-retest variability. A total of 2152 optic nerve images were available from both methods (also 371 from the reference camera but not the smartphone, 170 from the smartphone but not the reference camera, and 227 from neither the reference camera nor the smartphone). Bland-Altman analysis revealed a mean difference of 0.02 (95%CI, −0.21 to 0.17) and a weighted κ coefficient of 0.69 (excellent agreement). The grades of an experienced retinal photographer were compared with those of a lay photographer (no health care experience before the study), and no observable difference in image acquisition quality was found. Nonclinical photographers using the low-cost smartphone adapter were able to acquire optic nerve images at a standard that enabled independent remote grading of the images comparable to those acquired using a desktop retinal camera operated by an ophthalmic assistant. The potential for task shifting and the detection of avoidable causes of blindness in the most at-risk communities makes this an attractive public health intervention

Development and Validation of a Smartphone-based Contrast Sensitivity Test.

PURPOSE: Contrast sensitivity (CS) testing is an important measure of visual function reflecting variations in everyday visual experience in different conditions and helps to identify more subtle vision loss. However, it is only infrequently used. To make this more accessible, we have developed and validated a smartphone-based CS test. METHODS: A new tumbling-E smartphone-based CS test was developed, Peek Contrast Sensitivity (PeekCS). This was field tested and refined through several iterations. Reference standard was a tumbling-E Pelli-Robson CS test (PRCS). The validation study was conducted in community clinics in Ethiopia. Test-retest variability was measured for both PRCS and PeekCS. PRCS and PeekCS were then compared. Correlation coefficients and 95% confidence intervals (CIs) were calculated; 95% limits of agreement were calculated and displayed on Bland-Altman plots. RESULTS: PeekCS showed strong repeatability (correlation coefficient: 0.93; 95% CI: 0.91-0.95), which was comparable with PRCS (correlation coefficient: 0.96; 95% CI: 0.95-0.97). The 95% limit of agreement for test-retest variability of PRCS and PeekCS were -0.20 to 0.21 and -0.31 to 0.29, respectively. PRCS and PeekCS were highly correlated: 0.94 (95% CI: 0.93-0.95); 95% limits of agreement -0.27 to 0.29; and mean difference 0.010 (95% CI: -0.001 to 0.022). PeekCS had a faster testing time (44.6 seconds) than PRCS (48.6 seconds): mean difference -3.98 (95% CI: -5.38 to -2.58); P < 0.001. CONCLUSIONS: The smartphone-based PeekCS is a repeatable and rapid test, providing results that are highly comparable with the commonly used PRCS test. TRANSLATIONAL RELEVANCE: PeekCS provides an accessible and easy to perform alternative for CS testing, particularly in the community setting

Development and Validation of a Digital (Peek) Near Visual Acuity Test for Clinical Practice, Community-Based Survey, and Research

PURPOSE: Unaddressed near vision impairment (NVI) affects more than 500 million people. Testing near vision is necessary to identify those in need of services. To make such testing readily accessible, we have developed and validated a new smartphone-based near visual acuity (NVA) test: Peek Near Vision (PeekNV). METHODS: Two forms of the PeekNV test were developed: (1) quantitative measurement of NVA, and (2) binary screening test for presence or absence of NVI. The validity study was carried out with 483 participants in Sagarmatha Choudhary Eye Hospital, Lahan, Nepal, using a conventional Tumbling "E" Near Point Vision Chart as the reference standard. Bland-Altman limits of agreement (LoA) were used to evaluate test agreement and test-retest repeatability. NVI screening was assessed using Cohen's kappa coefficient, sensitivity, and specificity. RESULTS: The mean difference between PeekNV and chart NVA results was 0.008 logMAR units (95% confidence interval [CI], -0.005 to 0.021) in right eye data, and the 95% LoA between PeekNV and chart testing were within 0.235 and -0.218 logMAR. As a NVI screening tool, the overall agreement between tests was 92.9% (κ = 0.85). The positive predictive value of PeekNV was 93.2% (95% CI, 89.6% to 96.9%), and the negative predictive value 92.7% (95% CI, 88.9% to 96.4%). PeekNV had a faster NVI screening time (11.6 seconds; 95% CI, 10.5 to 12.6) than the chart (14.9 seconds; 95% CI, 13.5 to 16.2; P < 0.001). CONCLUSIONS: The PeekNV smartphone-based test produces rapid NVA test results, comparable to those of an accepted NV test. TRANSLATIONAL RELEVANCE: PeekNV is a validated, reliable option for NV testing for use with smartphones or digital devices

Access to community-based eye services in Meru, Kenya: a cross-sectional equity analysis

BACKGROUND: Over 80% of blindness in Kenya is due to curable or preventable causes and 7.5 m Kenyans currently need eye services. Embedding sociodemographic data collection into screening programmes could help identify the groups facing systematic barriers to care. We aimed to determine the sociodemographic characteristics that were associated with access among patients diagnosed with an eye problem and referred for treatment in the Vision Impact Programme, currently operating in Meru County. METHOD: We used an embedded, pragmatic, cross-sectional design. A list of sociodemographic questions was developed with input from key stakeholders. The final question set included the following domains: age, gender, religion, marital status, disability, education, occupation, income, housing, assets, and health insurance. These were integrated into an app that is used to screen, refer, and check-in (register) participants within a major eye screening programme. We gathered data from 4,240 people who screened positive and were referred to their local outreach treatment clinic. We used logistic regression to identify which groups were facing the greatest barriers to accessing care. RESULTS: A quarter of those screened between April - July 2023 were found to have an eye problem and were referred, however only 46% of these people were able to access care. In our fully adjusted model, at the 0.05 level there were no statistically significant differences in the odds of attendance within the domains of disability, health insurance, housing, income, or religion. Strong evidence (p < 0.001) was found of an association between access and age, gender, and occupation; with males, younger adults, and those working in sales, services and manual jobs the least likely to receive care. CONCLUSIONS: Access to essential eye services is low and unequal in Meru, with less than a third of those aged 18-44 receiving the care they need. Future work should explore the specific barriers faced by this group

Design of ophthalmic equipment for low-income countries, a workflow perspective

Global proliferation of mobile technology presents huge opportunities for healthcare, specifically regards improving operational efficiency, lowering costs, extending health systems' reach and improving adherence and acceptance. Foremost of reasons cited as prohibiting advancement of mHealth technologies from pilot to widespread adoption is paucity of data concerning the full impact on the health system's workflow.;The aim of this thesis is to investigate whether workflow modelling techniques, established in other fields, might be applied to the field of mHealth to address the lack of knowledge concerning its full health-system impact. Thus, it seeks to provide information that will help reduce the number of mHealth designs that fail to produce results in clinical pilots that lead to adoption at scale.;Data on the operation of proposed mHealth designs for use in community eye screening and retinopathy of prematurity screening were sourced from the scientific literature, technical datasheets and lab-based testing in the first instance and then pilot studies embedded in a community eye health study in Nakuru, Kenya. This included the design and prototyping of a novel smartphone-based ophthalmoscope appropriate for community settings in low-income countries.;The data acquired were analysed using standard statistical techniques. These analyses were then used to build stochastic timed coloured Petri-nets.;Classical statistical simulations based on these models generated data concerning the optimal design, workflow placement and usage of the mHealth devices. The data relating to retinopathy of prematurity screening specifically was used to design a tablet-computer operated, portable fundus camera for screening of preterm infants.;The results suggest that several mHealth design parameters have optimal values at odds with many of the piloted technologies that have failed to achieve adoption within the respective scenarios. It is recommended that further research, extending the techniques discussed and validating their effectiveness at producing designs appropriate for scale, be undertaken.Global proliferation of mobile technology presents huge opportunities for healthcare, specifically regards improving operational efficiency, lowering costs, extending health systems' reach and improving adherence and acceptance. Foremost of reasons cited as prohibiting advancement of mHealth technologies from pilot to widespread adoption is paucity of data concerning the full impact on the health system's workflow.;The aim of this thesis is to investigate whether workflow modelling techniques, established in other fields, might be applied to the field of mHealth to address the lack of knowledge concerning its full health-system impact. Thus, it seeks to provide information that will help reduce the number of mHealth designs that fail to produce results in clinical pilots that lead to adoption at scale.;Data on the operation of proposed mHealth designs for use in community eye screening and retinopathy of prematurity screening were sourced from the scientific literature, technical datasheets and lab-based testing in the first instance and then pilot studies embedded in a community eye health study in Nakuru, Kenya. This included the design and prototyping of a novel smartphone-based ophthalmoscope appropriate for community settings in low-income countries.;The data acquired were analysed using standard statistical techniques. These analyses were then used to build stochastic timed coloured Petri-nets.;Classical statistical simulations based on these models generated data concerning the optimal design, workflow placement and usage of the mHealth devices. The data relating to retinopathy of prematurity screening specifically was used to design a tablet-computer operated, portable fundus camera for screening of preterm infants.;The results suggest that several mHealth design parameters have optimal values at odds with many of the piloted technologies that have failed to achieve adoption within the respective scenarios. It is recommended that further research, extending the techniques discussed and validating their effectiveness at producing designs appropriate for scale, be undertaken

How the smartphone is driving the eye-health imaging revolution

The digitization of ophthalmic images has opened up a number of exciting possibilities within eye care such as automated pathology detection, as well as electronic storage and transmission. However, technology capable of capturing digital ophthalmic images remains expensive. We review the latest progress in creating ophthalmic imaging devices based around smartphones, which are readily available to most practicing ophthalmologists and other medical professionals. If successfully developed to be inexpensive and to offer high-quality imaging capabilities, these devices will have huge potential for disease detection and reduction of preventable blindness across the globe. We discuss the specific implications of such devices in high-, middle-and low-income settings