Red and green defocus curves and duochrome test in different age groups / Curvas de desenfoque rojo y verde y prueba de duocromo en diferentes grupos de edad

Purpose: To compare the eye defocus curves (DCs) obtained with stimuli on red, green, and white backgrounds and to investigate the applicability of the duochrome test (DT) in different age groups. Methods: 12 elderly (ELD: 59.3 ± 3.9 years) and 8 young (YG: 22.1 ± 1.1 years) subjects were recruited. An optometric assessment with the DT was carried out to obtain the subjective refraction at distance. DCs at distance on green, white, and red backgrounds were measured and the following parameters were deduced: dioptric difference between red-green, green-white, red-white focal positions (minima of the DCs), best corrected visual acuity (BCVA), and widths of the DCs for red, green, and white. Results: The DC difference between the green-white focal positions (mean ± standard deviation) was -0.12±0.17 diopters (D) (ELD, p = 0.012) and -0.11±0.12 D (YG, p = 0.039), while the red-white difference was not statistically significant. The DC red-green difference was 0.20±0.16 D (ELD, p = 0.002) and 0.18±0.18 D (YG, p = 0.008). The ELD BCVA with green background was significantly worse than BCVA with red (p = 0.007) and white (p = 0.007). The mean value of the DC's width in ELD for green (1.01±0.36 D) was higher than for red (0.77±0.21 D) and for white (0.84±0.35 D), but with no statistical significance. Conclusion: Both age groups showed a slight focusing preference for red when using white light. Moreover, ELD showed a worse BCVA with a green compared to a red background. Despite these results deduced by DC analyses, these aspects do not compromise the possibility of using the DT in clinical practice both in the young and in the elderly. Furthermore, the difference of about 0.20 D between red-green DC in both groups confirms the clinical appropriateness of the widespread use of 0.25 D step as the standard minimum difference in power between correcting lenses.(AU)

Liquid-Driven Microinjection System for Precise Fundus Injection.

Microinjection is usually applied to the treatment of some retinal disorders, such as retinal vein cannulation and displaced submacular hemorrhage. Currently, the microinjection procedure is usually performed by using the viscous fluid control of a standard vitrectomy system, which applies a fixed air pressure through foot pedal activation. The injection process with the fixed pressure is uncontrollable and lacks feedback, the high flow rate of the injected drug may cause damage to the fundus tissue. In this paper, a liquid-driven microinjection system with a flow sensor is designed and developed specifically for fundus injection. In addition, a PID sliding mode control (SMC) method is proposed to achieve precise injection in the injection system. The experimental results of fundus simulation injection demonstrate that the microinjection system meets the requirements of fundus injection and reduces the impact of the injection process on the fundus tissue.

Sustained release gel based on CT image inspection for treatment of diabetes fundus macular lesions.

Currently, slow-release gel therapy is considered to be an effective treatment for fundus macular disease, but the lack of effective evaluation methods limits its clinical application. Therefore, the purpose of this study was to investigate the application and clinical effect of slow-release gel based on CT image examination in the treatment of diabetic fundus macular disease. CT images of fundus macular lesions were collected in a group of diabetic patients. Then the professional image processing software is used to process and analyze the image and extract the key parameters. A slow-release gel was designed and prepared, and applied to the treatment of diabetic fundus macular disease. CT images before and after treatment were compared and analyzed, and the effect of slow-release gel was evaluated. In a certain period of time after treatment, the lesion size and lesion degree of diabetic fundus macular disease were significantly improved by using slow-release gel therapy with CT image examination. No significant adverse reactions or complications were observed during the treatment. This indicates that the slow-release gel based on CT image examination is a safe, effective and feasible treatment method for diabetic fundus macular disease. This method can help improve the vision and quality of life of patients, and provide a new idea and plan for clinical treatment.

Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images.

Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granu la ̲ r Neuron-le v ̲ el Expl a ̲ iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer's Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.

A novel vector field analysis for quantitative structure changes after macular epiretinal membrane surgery.

The aim of this study was to introduce novel vector field analysis for the quantitative measurement of retinal displacement after epiretinal membrane (ERM) removal. We developed a novel framework to measure retinal displacement from retinal fundus images as follows: (1) rigid registration of preoperative retinal fundus images in reference to postoperative retinal fundus images, (2) extraction of retinal vessel segmentation masks from these retinal fundus images, (3) non-rigid registration of preoperative vessel masks in reference to postoperative vessel masks, and (4) calculation of the transformation matrix required for non-rigid registration for each pixel. These pixel-wise vector field results were summarized according to predefined 24 sectors after standardization. We applied this framework to 20 patients who underwent ERM removal to obtain their retinal displacement vector fields between retinal fundus images taken preoperatively and at postoperative 1, 4, 10, and 22 months. The mean direction of displacement vectors was in the nasal direction. The mean standardized magnitudes of retinal displacement between preoperative and postoperative 1 month, postoperative 1 and 4, 4 and 10, and 10 and 22 months were 38.6, 14.9, 7.6, and 5.4, respectively. In conclusion, the proposed method provides a computerized, reproducible, and scalable way to analyze structural changes in the retina with a powerful visualization tool. Retinal structural changes were mostly concentrated in the early postoperative period and tended to move nasally.

Using generative AI to investigate medical imagery models and datasets.

BACKGROUND: AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren't yet known to experts. METHODS: In this paper, we present a workflow for generating hypotheses to understand which visual signals in images are correlated with a classification model's predictions for a given task. This approach leverages an automatic visual explanation algorithm followed by interdisciplinary expert review. We propose the following 4 steps: (i) Train a classifier to perform a given task to assess whether the imagery indeed contains signals relevant to the task; (ii) Train a StyleGAN-based image generator with an architecture that enables guidance by the classifier ("StylEx"); (iii) Automatically detect, extract, and visualize the top visual attributes that the classifier is sensitive towards. For visualization, we independently modify each of these attributes to generate counterfactual visualizations for a set of images (i.e., what the image would look like with the attribute increased or decreased); (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, present the discovered attributes and corresponding counterfactual visualizations to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health (e.g., whether the attributes correspond to known patho-physiological or socio-cultural phenomena, or could be novel discoveries). FINDINGS: To demonstrate the broad applicability of our approach, we present results on eight prediction tasks across three medical imaging modalities-retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples where many of the automatically-learned attributes clearly capture clinically known features (e.g., types of cataract, enlarged heart), and demonstrate automatically-learned confounders that arise from factors beyond physiological mechanisms (e.g., chest X-ray underexposure is correlated with the classifier predicting abnormality, and eye makeup is correlated with the classifier predicting low hemoglobin levels). We further show that our method reveals a number of physiologically plausible, previously-unknown attributes based on the literature (e.g., differences in the fundus associated with self-reported sex, which were previously unknown). INTERPRETATION: Our approach enables hypotheses generation via attribute visualizations and has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models, as well as debug and design better datasets. Though not designed to infer causality, importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors, and hence interdisciplinary perspectives are critical in these investigations. Finally, we will release code to help researchers train their own StylEx models and analyze their predictive tasks of interest, and use the methodology presented in this paper for responsible interpretation of the revealed attributes. FUNDING: Google.

Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading.

BACKGROUND AND OBJECTIVE: Early detection and grading of Diabetic Retinopathy (DR) is essential to determine an adequate treatment and prevent severe vision loss. However, the manual analysis of fundus images is time consuming and DR screening programs are challenged by the availability of human graders. Current automatic approaches for DR grading attempt the joint detection of all signs at the same time. However, the classification can be optimized if red lesions and bright lesions are independently processed since the task gets divided and simplified. Furthermore, clinicians would greatly benefit from explainable artificial intelligence (XAI) to support the automatic model predictions, especially when the type of lesion is specified. As a novelty, we propose an end-to-end deep learning framework for automatic DR grading (5 severity degrees) based on separating the attention of the dark structures from the bright structures of the retina. As the main contribution, this approach allowed us to generate independent interpretable attention maps for red lesions, such as microaneurysms and hemorrhages, and bright lesions, such as hard exudates, while using image-level labels only. METHODS: Our approach is based on a novel attention mechanism which focuses separately on the dark and the bright structures of the retina by performing a previous image decomposition. This mechanism can be seen as a XAI approach which generates independent attention maps for red lesions and bright lesions. The framework includes an image quality assessment stage and deep learning-related techniques, such as data augmentation, transfer learning and fine-tuning. We used the architecture Xception as a feature extractor and the focal loss function to deal with data imbalance. RESULTS: The Kaggle DR detection dataset was used for method development and validation. The proposed approach achieved 83.7 % accuracy and a Quadratic Weighted Kappa of 0.78 to classify DR among 5 severity degrees, which outperforms several state-of-the-art approaches. Nevertheless, the main result of this work is the generated attention maps, which reveal the pathological regions on the image distinguishing the red lesions and the bright lesions. These maps provide explainability to the model predictions. CONCLUSIONS: Our results suggest that our framework is effective to automatically grade DR. The separate attention approach has proven useful for optimizing the classification. On top of that, the obtained attention maps facilitate visual interpretation for clinicians. Therefore, the proposed method could be a diagnostic aid for the early detection and grading of DR.

Longitudinal changes in optic disc cupping from the baseline in chiasmal lesion optic neuropathy and glaucoma.

We aimed to investigate the changes in cupping in chiasmal lesion optic neuropathy (chON) compared to baseline optic disc and glaucoma. We used a novel study design to enroll patients who had fundus photographs incidentally taken during routine health check-ups prior to the onset of optic neuropathy. In 31 eyes (21 patients) with chON and 33 eyes (30 patients) with glaucoma, we investigated the change in cup-to-disc (C/D) area from the baseline to overt cupping using flicker analysis. Compared to the baseline, 23 eyes (74.2%) had increased cup size and 3 (9.7%) had vascular configuration changes in the chONgroup; in contrast, all glaucoma eyes exhibited changes in cup size and vascular configuration. The increase in C/D area ratio was significantly smaller in chON (0.04 ± 0.04) compared to glaucoma (0.10 ± 0.04, P < 0.001); the minimum residual neuroretinal rim width showed a more pronounced difference (29.7 ± 8.2% vs 7.1 ± 3.9%, P < 0.001). The changes distributed predominantly towards the nasal direction in chON, contrasting the changes to the arcuate fibers in glaucoma. In conclusion, our results provide the first longitudinal evidence of true pathological cupping in chONcompared to photographically disease-free baseline. The marked difference in the residual minimum rim width reaffirms the importance of rim obliteration in the differential diagnosis between the two diseases.

Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer-Based Learning Across Multiple Color-Spaces.

Purpose: The assessment of retinal image (RI) quality holds significant importance in both clinical trials and large datasets, because suboptimal images can potentially conceal early signs of diseases, thereby resulting in inaccurate medical diagnoses. This study aims to develop an automatic method for Retinal Image Quality Assessment (RIQA) that incorporates visual explanations, aiming to comprehensively evaluate the quality of retinal fundus images (RIs). Methods: We developed an automatic RIQA system, named Swin-MCSFNet, utilizing 28,792 RIs from the EyeQ dataset, as well as 2000 images from the EyePACS dataset and an additional 1,000 images from the OIA-ODIR dataset. After preprocessing, including cropping black regions, data augmentation, and normalization, a Swin-MCSFNet classifier based on the Swin-Transformer for multiple color-space fusion was proposed to grade the quality of RIs. The generalizability of Swin-MCSFNet was validated across multiple data centers. Additionally, for enhanced interpretability, a Score-CAM-generated heatmap was applied to provide visual explanations. Results: Experimental results reveal that the proposed Swin-MCSFNet achieves promising performance, yielding a micro-receiver operating characteristic (ROC) of 0.93 and ROC scores of 0.96, 0.81, and 0.96 for the "Good," "Usable," and "Reject" categories, respectively. These scores underscore the accuracy of RIQA based on Swin-MCSF in distinguishing among the three categories. Furthermore, heatmaps generated across different RIQA classification scores and various color spaces suggest that regions in the retinal images from multiple color spaces contribute significantly to the decision-making process of the Swin-MCSFNet classifier. Conclusions: Our study demonstrates that the proposed Swin-MCSFNet outperforms other methods in experiments conducted on multiple datasets, as evidenced by the superior performance metrics and insightful Score-CAM heatmaps. Translational Relevance: This study constructs a new retinal image quality evaluation system, which will contribute to the subsequent research of retinal images.

[Paying attention to neuropathic changes in high myopia].

The incidence of myopia is high in China. The proportion of high myopia is also high in the myopic population. High myopia is associated with multiple fundus changes, among which the neuropathic damage is usually ignored, and thus there has been limited clinical research on the pathogenesis, standard follow-up and effective treatment of optic neuropathy in high myopia. This article focuses on the types of high myopia-associated neuropathic changes, the quantitive imaging of neuropathic damage, and the need of relevant cohort studies and pathogenesis research, aiming to attract more attention to optic neuropathic changes in high myopia.

Retinal Changes in Patients With Covid-19 and Different Expressiveness of Metabolic Changes.

AIMS:  To study the relationship between the severity of COVID-induced metabolic changes and the structure and frequency of retinal changes, according to funduscopy data in patients with different clinical courses of COVID-19. MATERIALS AND METHODS:  117 patients with COVID-19 were examined. While examining patients, severity of the course of COVID-19, the expressiveness of changes in the metabolic status were determined; fundus image registration was performed with portable fundus cameras Pictor Plus Fundus Camera and VistaView (Volk Optical). RESULTS:  As a result of the research, retinal changes were found in 49 (41.9 %) patients with COVID-19. In 8 (16.3 %) cases, clinically significant (vitreous hemorrhage, prethrombosis of the central retinal vein or branches of the central retinal vein, thrombosis of the central retinal vein or branches of the central retinal vein) COVID-induced retinal and ophthalmological changes were observed, which caused a decrease in visual acuity. In 41 (83.7 %) cases, clinically insignificant changes (cotton wool spots, narrowed retinal vessels, intraretinal and petechial hemorrhages, tortuosity and dilatation of retinal venules) COVID-induced retinal changes were observed. Clinically significant retinal changes occur in patients with a statistically significantly higher level of D-dimer and a greater percentage of lung parenchyma lesion than in the group of patients with clinically insignificant retinal changes (p < 0.05). CONCLUSIONS:  The structure of retinal changes in patients with COVID-19 correlates with the severity of the clinical course of the disease and changes in the metabolic status of patients. Metabolic changes are correlated with retinal changes and can be predictive for preventing general vascular complications in COVID-19.

GravityNet for end-to-end small lesion detection.

This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be "attracted" by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.

Early detection of glaucoma integrated with deep learning models over medical devices.

The early detection of some diseases can be a decisive factor in postponing or stabilizing their most adverse effects on the people who suffer from them. In the case of glaucoma, which is an ocular pathology that is the second leading cause of blindness in the world, early detection can make the difference between a patient's complete losses of vision, or preserve their sight, as well as improve their subsequent treatment. It is for this reason that there are currently medical campaigns for the early detection of pathologies with these characteristics in a certain study population, called screening, which have shown very good results. In addition, the application of telemedicine to these processes has allowed remote evaluation of cases by clinical experts and numerous initiatives have emerged for its use in new screening strategies. On the other hand, biomedical image processing techniques based on deep learning have undergone great development in recent years, and there are several works that have demonstrated their possible application in the automatic detection of glaucoma with fundus images. The article has consisted of the development of a web platform that integrates both scenarios: on the one hand, the remote evaluation of fundus images by medical specialists, and on the other, the application of a tool based on Deep Learning for the automatic detection of glaucoma in the case studies.

Evaluating imaging repeatability of fully self-service fundus photography within a community-based eye disease screening setting.

PURPOSE: This study aimed to investigate the imaging repeatability of self-service fundus photography compared to traditional fundus photography performed by experienced operators. DESIGN: Prospective cross-sectional study. METHODS: In a community-based eye diseases screening site, we recruited 65 eyes (65 participants) from the resident population of Shanghai, China. All participants were devoid of cataract or any other conditions that could potentially compromise the quality of fundus imaging. Participants were categorized into fully self-service fundus photography or traditional fundus photography group. Image quantitative analysis software was used to extract clinically relevant indicators from the fundus images. Finally, a statistical analysis was performed to depict the imaging repeatability of fully self-service fundus photography. RESULTS: There was no statistical difference in the absolute differences, or the extents of variation of the indicators between the two groups. The extents of variation of all the measurement indicators, with the exception of the optic cup area, were below 10% in both groups. The Bland-Altman plots and multivariate analysis results were consistent with results mentioned above. CONCLUSIONS: The image repeatability of fully self-service fundus photography is comparable to that of traditional fundus photography performed by professionals, demonstrating promise in large-scale eye disease screening programs.

Personalized Lens Correction Improves Quantitative Fundus Autofluorescence Analysis.

Purpose: Quantitative fundus autofluorescence (QAF) currently deploys an age-based score to correct for lens opacification. However, in elderly people, lens opacification varies strongly between individuals of similar age, and innate lens autofluorescence is not included in the current correction formula. Our goal was to develop and compare an individualized formula. Methods: One hundred thirty participants were examined cross-sectionally, and a subset of 30 participants received additional multimodal imaging 2-week post-cataract-surgery. Imaging included the Scheimpflug principle, anterior chamber optical coherence tomography (AC-OCT), lens quantitative autofluorescence (LQAF), and retinal QAF imaging. Among the subset, least absolute shrinkage and selection operator regression and backward selection was implemented to determine which lens score best predicts the QAF value after lens extraction. Subsequently, a spline mixed model was applied to the whole cohort to quantify the influence of LQAF and Scheimpflug on QAF. Results: Age and LQAF measurements were found to be the most relevant variables, whereas AC-OCT measurements and Scheimpflug were eliminated by backward selection. Both an increase in Scheimpflug and LQAF values were associated with a decrease in QAF. The prediction error of the spline model (mean absolute error [MAE] ± standard deviation) of 32.2 ± 23.4 (QAF a.u.) was markedly lower compared to the current age-based formula MAE of 96.1 ± 93.5. Both smooth terms, LQAF (P < 0.01) and Scheimpflug (P < 0.001), were significant for the spline mixed model. Conclusions: LQAF imaging proved to be the most predictive for the impact of the natural lens on QAF imaging. The application of lens scores in the clinic could improve the accuracy of QAF imaging interpretation and might allow including aged patients in future QAF studies.

[Preclinical diagnostics of von Hippel-Lindau syndrome in a child].

The description of the child aged 5 months with the von Hippel-Lindau syndrome without any manifestations of this syndrome is presented. The reason for the molecular genetic examination was the presence of cases of this syndrome in the family (mother and sister). The heterozygous variant c.355T>C p.F119L was found in the VHL gene. An objective examination revealed no pathology. A comprehensive laboratory and instrumental examination aimed at searching for components of the von Hippel-Lindau syndrome, including a blood test for metanephrines and normetanephrines, ultrasound of the abdominal organs, examination of the fundus, also did not reveal any abnormalities. Given the results of molecular genetic diagnosis, the child remains under observation and will undergo regular examinations to identify components of the von Hippel-Lindau syndrome, including blood/urine tests for normetanephrines.

New proposal for a multimodal imaging approach for the subclinical detection of hydroxychloroquine-induced retinal toxicity in patients with systemic lupus erythematosus.

OBJECTIVE: To compare multimodal structural and functional diagnostic methods in patients with systemic lupus erythematosus (SLE) treated with hydroxychloroquine, to identify the best complementary approach for detecting subclinical retinal toxicity. METHODS: A cross-sectional, unicentric study was conducted on patients with SLE treated with hydroxychloroquine. Each patient underwent a comprehensive ophthalmic evaluation, comprising structural tests (spectral-domain optical coherence tomography (SD-OCT), en face OCT, en face OCT angiography (OCTA), fundus autofluorescence (FAF)) and functional tests (automated perimetry for visual field (VF) testing, multifocal electroretinography (mfERG)). A diagnosis of macular toxicity required the presence of abnormalities in at least one structural and functional test. The Kappa Concordance Index was used to assess the concordance among the different tests in detecting potential macular toxicity-associated alterations. RESULTS: Sixty-six patients with SLE (132 eyes) were consecutively enrolled. Four (6.1%) patients developed subclinical hydroxychloroquine-induced retinal toxicity without visual acuity impairment. The proportion of abnormal results was 24% for both en face OCT and en face OCTA. Regarding functional analysis, VF was less specific than mfERG in detecting subclinical retinal toxicity (VF specificity 47.5%). En face OCT and en face OCTA structural findings showed better concordance, with a kappa index >0.8, and both identified the same cases of toxicity as FAF. CONCLUSION: Although structural OCT and VF are frequently used to screen for hydroxychloroquine-induced retinal toxicity, our findings suggest that a combination of mfERG, en face OCT and en face OCTA could improve the diagnostic accuracy for subclinical retinal damage. This study emphasises the importance of a multimodal imaging strategy to promptly detect signs of hydroxychloroquine-induced retinal toxicity.

Assessing Diabetic Retinopathy Staging With AI: A Comparative Analysis Between Pseudocolor and LED Imaging.

Purpose: To compare the diagnostic performance of artificial intelligence (AI)-based diabetic retinopathy (DR) staging system across pseudocolor, simulated white light (SWL), and light-emitting diode (LED) camera imaging modalities. Methods: A cross-sectional investigation involved patients with diabetes undergoing imaging with an iCare DRSplus confocal LED camera and an Optos confocal, ultra-widefield pseudocolor camera, with and without SWL. Macula-centered and optic nerve-centered 45 × 45-degree photographs were processed using EyeArt v2.1. Human graders established the ground truth (GT) for DR severity on dilated fundus exams. Sensitivity and weighted Cohen's weighted kappa (wκ) were calculated. An ordinal generalized linear mixed model identified factors influencing accurate DR staging. Results: The study included 362 eyes from 189 patients. The LED camera excelled in identifying sight-threatening DR stages (sensitivity = 0.83, specificity = 0.95 for proliferative DR) and had the highest agreement with the GT (wκ = 0.71). The addition of SWL to pseudocolor imaging resulted in decreased performance (sensitivity = 0.33, specificity = 0.98 for proliferative DR; wκ = 0.55). Peripheral lesions reduced the likelihood of being staged in the same or higher DR category by 80% (P < 0.001). Conclusions: Pseudocolor and LED cameras, although proficient, demonstrated non-interchangeable performance, with the LED camera exhibiting superior accuracy in identifying advanced DR stages. These findings underscore the importance of implementing AI systems trained for ultra-widefield imaging, considering the impact of peripheral lesions on correct DR staging. Translational Relevance: This study underscores the need for artificial intelligence-based systems specifically trained for ultra-widefield imaging in diabetic retinopathy assessment.

Prevalence of Myopic Maculopathy Among the Very Old: The Ural Very Old Study.

Purpose: To assess the prevalence of myopic macular degeneration (MMD) in very old individuals. Methods: The population-based Ural Very Old Study (UVOS) included 1526 (81.1%) of 1882 eligible inhabitants aged ≥85 years. Assessable fundus images were available for 930 (60.9%) individuals (mean age, 88.6 ± 2.7 years). MMD was defined by macular patchy atrophies (i.e., MMD stage 3 and 4 as defined by the Pathologic Myopia Study Group). Results: MMD prevalence was 21 of 930 (2.3%; 95% CI, 1.3-3.3), with 10 individuals (1.1%; 95% CI, 0.4-1.7) having MMD stage 3 and 11 participants (1.2%; 95% CI, 0.5-1.9) MMD stage 4 disease. Within MMD stage 3 and 4, prevalence of binocular moderate to severe vision impairment was 4 of 10 (40%; 95% CI, 31-77) and 7 of 11 (64%; 95% CI, 30-98), respectively, and the prevalence of binocular blindness was 2 of 10 (20%; 95% CI, 0-50) and 3 of 11 (27%; 95% CI, 0-59), respectively. In minor myopia (axial length, 24.0 to <24.5 mm), moderate myopia (axial length, 24.5 to <26.5 mm), and high myopia (axial length, ≥26.5 mm), MMD prevalence in the right eyes was 0 of 46 eyes (0%), 3 of 40 eyes (8%; 95% CI, 0-16), and 7 of 9 (78%; 95% CI, 44-100), respectively; MMD prevalence in the left eyes was 1 in 48 eyes (2%; 95% CI, 0-6), 4 of 36 eyes (11%; 95% CI, 0-22), and 3 of 4 eyes (75%; 95% CI, 0-100), respectively. In multivariable analysis, a higher MMD prevalence (odds ratio, 8.89; 95% CI, 3.43-23.0; P < 0.001) and higher MMD stage (beta, 0.45; B, 19; 95% CI, 0.16-0.22; P < 0.001) were correlated with longer axial length but not with any other ocular or systemic parameter. Conclusions: MMD prevalence (stages 3 and 4) in very old individuals increased 8.89-fold for each mm axial length increase, with a prevalence of ≥75% in highly myopic eyes. In old age, highly myopic individuals have a high risk of eventually developing MMD with marked vision impairment.