London Vision Clinic

The number of older adults diagnosed with vision loss in Canada, and globally, is growing. Older adults with age-related vision loss (ARVL), including macular degeneration, glaucoma, and diabetic retinopathy, experience a reduced capacity to engage with their communities because their opportunities to access and engage with their environments are greatly reduced. This can lead to feelings of abandonment, enhanced fear, and social isolation, which can have determinantal impacts on physical, social, and psychological health and well-being. This critical ethnographic study moves beyond exclusively identifying physical barriers to community mobility, towards addressing the physical, political, social, and pragmatic barriers to community mobility that are limiting for older adults aging with, and into, vision loss. As a research collective consisting of older adults with vision loss, low vision service providers, policy makers, and Blind and sighted academics, we developed a critical ethnographic study which consists of three qualitative interviews. Our protocol outlines how data for this study were collected, including how methods were adapted to support people experiencing vision loss, analyzed, and managed to ensure best privacy practices. We also describe our approach to engaging in reflexive data analysis and interpretation, including collective data analysis sessions.

BackgroundWriting is an important yet underexplored area of rehabilitation for individuals with acquired vision loss. This study investigates the challenges older adults with vision impairment face when performing writing tasks, including both handwriting and typing using physical or virtual methods, and the impact on their emotional well-being and independence.MethodsSemi-structured interviews were conducted with ten participants aged 67 to 93, all with acquired vision loss. The interviews explored the writing tasks participants wished to perform, the specific challenges they encountered, successful writing experiences, and the interventions or alternative strategies used. The data was analysed using content analysis to identify key patterns and themes.ResultsParticipants reported difficulties with handwriting, particularly in maintaining legibility and writing in a straight line. Typing challenges included frequent keystroke errors and difficulty reading text on screens leading to frustration, embarrassment, and a diminished sense of independence, with some participants avoiding writing altogether. While assistive technologies, such as screen magnifiers and text-to-speech software, were used, their inconsistency and effectiveness varied, resulting in frustration.ConclusionsHandwriting and typing present challenges for older adults with acquired vision loss, impacting their emotional well-being and independence. Key outcome measures for successful writing tasks should prioritise legibility, accuracy, and ability to perform tasks independently. Current assistive technologies offer some support but are often inconsistent, underscoring the need for more effective, user-friendly interventions. Rehabilitation programs should focus on developing strategies and tools that enable individuals to achieve greater accuracy and independence in writing, with outcome measures aligned to these goals.

AimsTo develop a generative adversarial network (GAN) capable of generating realistic high-resolution anterior segment optical coherence tomography (AS-OCT) images.MethodsThis study included 142 628 AS-OCT B-scans from the American University of Beirut Medical Center. The Style and WAvelet based GAN architecture was trained to generate realistic AS-OCT images and was evaluated through the Fréchet Inception Distance (FID) Score and a blinded assessment by three refractive surgeons who were asked to distinguish between real and generated images. To assess the suitability of the generated images for machine learning tasks, a convolutional neural network (CNN) was trained using a dataset of real and generated images over a classification task. The generated AS-OCT images were then upsampled using an enhanced super-resolution GAN (ESRGAN) to achieve high resolution.ResultsThe generated images exhibited visual and quantitative similarity to real AS-OCT images. Quantitative similarity assessed using FID scored an average of 6.32. Surgeons scored 51.7% in identifying real versus generated images which was not significantly better than chance (p value >0.3). The CNN accuracy improved from 78% to 100% when synthetic images were added to the dataset. The ESRGAN upsampled images were objectively more realistic and accurate compared with traditional upsampling techniques by scoring a lower Learned Perceptual Image Patch Similarity of 0.0905 compared with 0.4244 of bicubic interpolation.ConclusionsThis study successfully developed and leveraged GANs capable of generating high-definition synthetic AS-OCT images that are realistic and suitable for machine learning and image analysis tasks.

Optical coherence tomography (OCT) and OCT angiography (OCTA) are widely used in the diagnosis of ophthalmic diseases. This study aims to provide a comprehensive bibliometric analysis of ophthalmologic OCT and OCT angiography. We retrieved publications on ophthalmic OCT and OCTA from 2003 to 2022 from the Web of Science Core Collection and used bibliometric tools to analyze and visualize the distribution, trend, and hotspots. In total, 20,817 articles written by 48,160 authors from 106 countries were selected. The number of publications has significantly increased. In the last two decades, the USA was the most productive country and received the highest citations. The most productive journal was Investigative Ophthalmology and Visual Science and received the highest number of citations. Moorfields Eye Hospital was the most productive institution. Bandello F published the most papers, while Spaide RF was the highest cited author. SPAIDE RF, 2011, AM J Ophthalmology was the most cited document. "OCT", "glaucoma" and "OCTA" were three hotspots in the last two decades. "Vessel density" and "deep learning" would be research hotspots in the future. The bibliometric analysis of ophthalmic OCT and OCTA research over the past two decades on keywords, authors, citations, hotspots and trends will provide global researchers with valuable information for future research and cooperation.

BackgroundOver 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.MethodWe 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.ResultsA 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.ConclusionsAccess 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.

Aims We aimed to develop convolutional neural networks (CNN) to detect cleaning and polypectomy phases during withdrawal to facilitate automated measurement of colonoscopy inspection time (CIT) [1].

To provide a metric to differentiate between hyperopic and myopic ablation of a prior LASIK treatment based on the corneal pachymetry profile after laser vision correction (LVC). Pachymetry data were retrospectively recovered from patients who had previous LASIK for refractive purposes between 2019 and 2020. Patients with any corneal disorder were excluded. Ablation spherical equivalent was predicted from the central to semiperipheral corneal thickness (CPT) ratio, both values were provided by using the Pentacam user interface software (UI), and values were computed from extracted raw pachymetry data. Data of 157 eyes of 81 patients were collected, of which data were analysed for 73 eyes of 73 patients to avoid concurrence of measurements in both eyes per subject (42% female; mean age 40.9; SD 12.8). The CPT ratio cutoff for distinction between myopic and hyperopic LASIK was 0.86 for Pentacam UI data. Sensitivity and specificity were 0.7 and 0.95, respectively. Accuracy increased with computation of the CPT ratio based on extracted raw data with sensitivity and specificity of 0.87 and 0.99, respectively. There was a marked linear correlation between the CPT ratio and the ablation spherical equivalent (R2 = 0.93). CPT ratio cutoffs can correctly classify if a cornea previously had a hyperopic versus myopic LASIK surgery and estimate the ablation spherical equivalent of such treatment. This could prove useful for increased accuracy of intraocular lens (IOL) calculations for patients with no historical data of their prior LVC surgery at the time of cataract surgery planning.