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)

Swept-source optical coherence tomography angiography findings in a case of primary vitreoretinal lymphoma over a three-year follow-up.

BACKGROUND: Vitreoretinal lymphoma (VRL) still represents a diagnostic challenge for retinal specialists. Early diagnosis and treatment are critical for a better prognosis. Several diagnostic tools have proven helpful in the identification of VRL abnormalities. However, swept-source OCT angiography (SS-OCT-A) findings and their long-term follow-up are yet to be explored. CASE PRESENTATION: a 42-year-old man presented with blurred vision in his left eye for 2 weeks. He denied any systemic symptoms. A multimodal imaging examination was performed, raising the clinical suspicion of VRL and guiding the ensuing diagnostic procedures. The patient underwent treatment and at the last FU visit three years later, no disease signs were present on fundus examination, nor on oncologic evaluation. Some novel SS-OCT-A features were identified, and uncommonly reported findings were examined over a long-term follow-up. At baseline multiple hyperreflective alterations were detected on the enface outer retina slabs and choriocapillary analysis revealed low reflectance areas in the foveal and parafoveal areas. One month after the first presentation, multiple hyperreflective retinal lesions in a vertical shape were detected on OCT which appeared on midretinal slabs of enface SS-OCT-A as hyperreflective spots mainly located near second-order retinal vessels. These alterations remarkably reduced after treatment. CONCLUSION: SS-OCT-A may be a useful imaging technique in the detection of VRL, providing ophthalmologists additional findings that assist the diagnosis and follow-up of this disease. This may prove useful for a more timely and precise diagnosis, prompt therapy, and treatment response monitoring. The original aspects found in this case may provide grounds for future studies, ultimately fostering a better understanding of the disease.

Vitiligo in a Patient Receiving Adalimumab for Idiopathic Uveitis.

In recent years, adalimumab has been increasingly used in the chronic treatment of non-infectious uveitis. This case report aimed to describe a drug-induced adverse event in a 34-year-old man who presented with blurred vision and floaters in the right eye and was being treated for intermediate uveitis. The patient had started topical treatment with a diagnosis of uveitis at another center. Best corrected visual acuity at presentation was 0.8 (decimal) in the right eye and 1.0 in the left eye. On examination, the anterior chamber in the right eye was clear, with anterior vitreous cells and mild haze, and snow banking and vitreous opacities in the inferior periphery. Fluorescein angiography (FA) showed hyperfluorescence in the right disc and leakage in the inferior periphery. As the inflammation did not resolve with local treatment, systemic cyclosporine was administered, after which the patient exhibited vomiting and weakness. Cyclosporine was discontinued and adalimumab treatment was started. On examination 5 months later, bilateral vitreous cells and mild vitreous opacity were noted, and FA showed mild leakage in the inferior periphery bilaterally. In addition, a depigmented patchy vitiligo lesion was observed on the chin. Due to the persistence of intraocular inflammation and on the recommendation of the dermatology clinic, adalimumab treatment was continued and topical tacrolimus was started for the lesion. On examination 3 months later, the inflammatory findings had resolved and there was no progression of the vitiligo lesion. The patient's treatment was continued. Taken together with the previous literature findings, no pathology was found in the patient's systemic examination, suggesting that this lesion was a side effect of the treatment. Ophthalmologists should be alert for this side effect in patients receiving adalimumab.

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.

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.

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.

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.

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.

Vasoproliferative Tumor Secondary to Sarcoidosis-Associated Intermediate Uveitis.

We report the visual and clinical outcomes of a middle-aged woman who presented with exudative retinal detachment (ERD) secondary to a vasoproliferative tumor (VPT) in an eye with sarcoidosis-associated intermediate uveitis. A 55-year-old woman previously diagnosed with sarcoidosis presented with decreased vision in the left eye (LE). Visual acuity in the LE was counting fingers. She had active vitritis, and a peripheral retinal vascular mass was noted in the superotemporal periphery. The mass was associated with ERD involving the posterior pole. The patient was managed with systemic and intravitreal steroids, and cyclosporine was subsequently added as a steroid-sparing agent. Because of recurrence of ERD, the patient underwent pars plana vitrectomy, and cryotherapy and laser photocoagulation were applied to the VPT. Two months postoperatively, visual acuity in the LE improved to 6/10. There was marked regression of the VPT and total resolution of the ERD. In conclusion, we report a favorable visual and clinical outcome in a patient with VPT-associated ERD who responded to a combination of medical therapy and surgical intervention. VPT may lead to different remote complications, so timely diagnosis of these tumors and proper management of their complications is warranted.

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.

[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.

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.

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.

[Preliminary study on automatic quantification and grading of leopard spots fundus based on deep learning technology].

Objective: To achieve automatic segmentation, quantification, and grading of different regions of leopard spots fundus (FT) using deep learning technology. The analysis includes exploring the correlation between novel quantitative indicators, leopard spot fundus grades, and various systemic and ocular parameters. Methods: This was a cross-sectional study. The data were sourced from the Beijing Eye Study, a population-based longitudinal study. In 2001, a group of individuals aged 40 and above were surveyed in five urban communities in Haidian District and three rural communities in Daxing District of Beijing. A follow-up was conducted in 2011. This study included individuals aged 50 and above who participated in the second 5-year follow-up in 2011, considering only the data from the right eye. Color fundus images centered on the macula of the right eye were input into the leopard spot segmentation model and macular detection network. Using the macular center as the origin, with inner circle diameters of 1 mm, 3 mm, and outer circle diameter of 6 mm, fine segmentation of the fundus was achieved. This allowed the calculation of the leopard spot density (FTD) and leopard spot grade for each region. Further analyses of the differences in ocular and systemic parameters among different regions' FTD and leopard spot grades were conducted. The participants were categorized into three refractive types based on equivalent spherical power (SE): myopia (SE<-0.25 D), emmetropia (-0.25 D≤SE≤0.25 D), and hyperopia (SE>0.25 D). Based on axial length, the participants were divided into groups with axial length<24 mm, 24-26 mm, and>26 mm for the analysis of different types of FTD. Statistical analyses were performed using one-way analysis of variance, Kruskal-Wallis test, Bonferroni test, and Spearman correlation analysis. Results: The study included 3 369 participants (3 369 eyes) with an average age of (63.9±10.6) years; among them, 1 886 were female (56.0%) and 1, 483 were male (64.0%). The overall FTD for all eyes was 0.060 (0.016, 0.163); inner circle FTD was 0.000 (0.000, 0.025); middle circle FTD was 0.030 (0.000, 0.130); outer circle FTD was 0.055 (0.009, 0.171). The results of the univariate analysis indicated that FTD in various regions was correlated with axial length (overall: r=0.38, P<0.001; inner circle: r=0.31, P<0.001; middle circle: r=0.36, P<0.001; outer circle: r=0.39, P<0.001), subfoveal choroidal thickness (SFCT) (overall: r=-0.69, P<0.001; inner circle: r=-0.57, P<0.001; middle circle: r=-0.68, P<0.001; outer circle: r=-0.72, P<0.001), age (overall: r=0.34, P<0.001; inner circle: r=0.30, P<0.001; middle circle: r=0.31, P<0.001; outer circle: r=0.35, P<0.001), gender (overall: r=-0.11, P<0.001; inner circle: r=-0.04, P<0.001; middle circle: r=-0.07, P<0.001; outer circle: r=-0.11, P<0.001), SE (overall: r=-0.20; P<0.001; inner circle: r=-0.19, P<0.001; middle circle: r=-0.20, P<0.001; outer circle: r=-0.20, P<0.001), uncorrected visual acuity (overall: r=-0.18, P<0.001; inner circle: r=-0.26, P<0.001; middle circle: r=-0.24, P<0.001; outer circle: r=-0.22, P<0.001), and body mass index (BMI) (overall: r=-0.11, P<0.001; inner circle: r=-0.13, P<0.001; middle circle: r=-0.14, P<0.001; outer circle: r=-0.13, P<0.001). Further multivariate analysis results indicated that different region FTD was correlated with axial length (overall: ß=0.020, P<0.001; inner circle: ß=-0.022, P<0.001; middle circle: ß=0.027, P<0.001; outer circle: ß=0.022, P<0.001), SFCT (overall: ß=-0.001, P<0.001; inner circle: ß=-0.001, P<0.001; middle circle: ß=-0.001, P<0.001; outer circle: ß=-0.001, P<0.001), and age (overall: ß=0.002, P<0.001; inner circle: ß=0.001, P<0.001; middle circle: ß=0.002, P<0.001; outer circle: ß=0.002, P<0.001). The distribution of overall (H=56.76, P<0.001), inner circle (H=72.22, P<0.001), middle circle (H=75.83, P<0.001), and outer circle (H=70.34, P<0.001) FTD differed significantly among different refractive types. The distribution of overall (H=373.15, P<0.001), inner circle (H=367.67, P<0.001), middle circle (H=389.14, P<0.001), and outer circle (H=386.89, P<0.001) FTD differed significantly among different axial length groups. Furthermore, comparing various levels of FTD with systemic and ocular parameters, significant differences were found in axial length (F=142.85, P<0.001) and SFCT (F=530.46, P<0.001). Conclusions: The use of deep learning technology enables automatic segmentation and quantification of different regions of theFT, as well as preliminary grading. Different region FTD is significantly correlated with axial length, SFCT, and age. Individuals with older age, myopia, and longer axial length tend to have higher FTD and more advanced FT grades.

[Clinical observation of the intraocular distribution characteristics of indocyanine green after epiretinal membrane peeling using a fluorescence detection system developed in Python].

Objective: To utilize a Python-based fluorescence area detection system to observe and quantitatively analyze the intraocular distribution characteristics and metabolic patterns of Indocyanine Green (ICG) following epiretinal membrane peeling. Methods: A prospective case series study was conducted on patients with idiopathic epiretinal membrane undergoing vitrectomy at West China Hospital of Sichuan University from March 2019 to March 2021. ICG staining was applied during surgery for peeling the epiretinal membrane and internal limiting membrane. Patients were followed up at 1 week, 1 month, 3 months, 6 months, and 12 months postoperatively, with assessments including best-corrected visual acuity, intraocular pressure, fundus photography, near-infrared fundus fluorescence imaging (NIR-FF), and optical coherence tomography (OCT). A Python-based ICG intraocular metabolism detection system was developed to measure the residual area of ICG fluorescence on NIR-FF, predict the ICG metabolic pattern equation, and correlate it with postoperative visual acuity and peripapillary retinal nerve fiber layer thickness. Results: A total of 64 patients (64 eyes) were included, with an average age of 64.6±8.4 years, including 25 males (39.1%) and 39 females (60.9%). Preoperative NIR-FF images showed no ICG strong fluorescence. At 1 week postoperatively, diffuse ICG strong fluorescence appeared in the posterior pole, and the internal limiting membrane removal area exhibited a ring-like weak fluorescence. Over time, ICG strong fluorescence was observed along the vascular arch and nerve fiber trajectory, gradually diminishing toward the optic disc, with residual ICG fluorescence still visible at the optic disc at 1 year. The Python-based ICG fluorescence area detection system effectively measured intraocular residual ICG area. A predictive equation for the 12-month residual ICG area was constructed through linear regression analysis (Residual ICG area=0.22 × Residual ICG area at 6 months, R2=16%, P=0.002). Except for a negative correlation between the ICG residual area at 1 month and postoperative visual acuity (P=0.017, r=-0.195), no correlation was found between intraocular ICG fluorescence residual area and postoperative visual acuity or peripapillary retinal nerve fiber layer thickness at other follow-up times (all P>0.05). Conclusions: In patients with idiopathic epiretinal membrane undergoing ICG staining for internal limiting membrane peeling, ICG exhibits characteristic metabolic processes in the eye, with strong fluorescence along the vascular arch and nerve fiber trajectory, gradually converging toward the optic disc over time. The Python-based ICG fluorescence area detection system provides a clear display of the intraocular distribution characteristics of ICG after epiretinal membrane peeling and serves as a tool for predicting the metabolic patterns of ICG in the eye.

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.

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.