Ultra-widefield Fundus Research Articles

Multimodal Imaging Study of Patients With Vitamin A Deficiency Retinopathy.

To describe multimodal imaging findings of vitamin A deficiency retinopathy. A retrospective study of patients with serum retinol < 0.3 mg/L. Fundus color photos, spectral domain-optical coherence tomography (SD-OCT), and fundus autofluorescence (FAF) were reviewed and, when available, electrophysiological tests were analyzed. Forty-five eyes (63.9 ± 15.7 years) were included. Ultra-widefield fundus photography showed drusen-like deposits (53.3%) and macular retinal pigment epithelium (RPE) mottling (40%). The deposits were hypoautofluorescent, and a perifoveal hyperautofluorescent ring was present in 8.9%. By SD-OCT, the ellipsoid zone had an irregular appearance (100%) and conical deposits anterior to the RPE (33.3%). Electroretinogram (ERG) (66.7%) showed a decrease in b-wave in the scotopic registers, and microperimetry (4.4%) showed decreased foveal sensitivity. After vitamin A supplementation, SD-OCT and FAF showed resolution of all findings. Forty percent of eyes had restoration of the scotopic registers in ERG and improved macular sensitivity by microperimetry (4.4%). Vitamin A deficiency causes a mild cone dysfunction in addition to the more severe absent rod response. [Ophthalmic Surg Lasers Imaging Retina 2023;54:330-336.].

X-Linked Retinoschisis Masquerading Uveitis.

X-linked retinoschisis (XLRS) shows features also seen in patients with uveitis and is recognized as an uveitis masquerade syndrome. This retrospective study aimed to describe characteristics of XLRS patients with an initial uveitis diagnosis and to contrast these to patients with an initial XLRS diagnosis. Patients referred to a uveitis clinic, which turned out to have XLRS (n = 4), and patients referred to a clinic for inherited retinal diseases (n = 18) were included. All patients underwent comprehensive ophthalmic examinations, including retinal imaging with fundus photography, ultra-widefield fundus imaging, and optical coherence tomography (OCT). In patients with an initial diagnosis of uveitis, a macular cystoid schisis was always interpreted as an inflammatory macular edema; vitreous hemorrhages were commonly interpreted as intraocular inflammation. Patients with an initial diagnosis of XLRS rarely (2/18; p = 0.02) showed vitreous hemorrhages. No additional demographic, anamnestic, and anatomical differences were found. An increased awareness of XLRS as a uveitis masquerade syndrome may facilitate early diagnosis and may prevent unnecessary therapies.

Widefield oct-angiography-based classification of sickle cell retinopathy.

To assess the capillary non-perfusion in different concentric sectors on widefield optical coherence tomography angiography (WF-OCTA) and to correlate the ratio of non-perfusion (RNP) to the severity of sickle cell retinopathy (SCR). This retrospective, cross-sectional study included eyes of patients with various sickle cell disease (SCD) genotypes having undergone WF-OCTA and ultra-widefield color fundus photography (UWF-CFP). Eyes were grouped as no SCR, non-proliferative SCR or proliferative SCR. RNP was assessed on WF-OCTA montage in different field-of-view (FOV) sectors centered on the fovea: 0-10-degrees circle excluding the foveal avascular zone, the 10-30-degrees circle excluding the optic nerve, the 30-60-degrees circle, and the full 60-degrees circle. Forty-two eyes of twenty-eight patients were included. Within each SCR group, mean RNP of the FOV 30-60 sector was higher than all other sectors (p < 0.05). Mean RNP of all sectors were significatively different between no SCR group and proliferative SCR group (p < 0.05). To distinguish no SCR versus non-proliferative SCR FOV 30-60 had a good sensitivity and specificity of 41.67% and 93.33%, respectively (cutoff RNP > 22.72%, AUC = 0.75, 95% CI 0.56-0.94, p = 0.028). To differentiate non-proliferative versus proliferative SCR, FOV 0-10 had good sensitivity and specificity of 33.33% and 91.67%, respectively (cutoff RNP > 18.09, AUC = 0.73, 95% CI 0.53 to 0.93, p = 0.041). To discern no SCR versus proliferative SCR, all sectors had optimal sensitivity and specificity (p < 0.05). WF OCTA-based RNP provides non-invasive diagnostic information regarding the presence and severity of SCR, and correlates with disease stage in certain FOV sectors.

Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images.

Background: Ultra-Wide-Field (UWF) fundus imaging is an essential diagnostic tool for identifying ophthalmologic diseases, as it captures detailed retinal structures within a wider field of view (FOV). However, the presence of eyelashes along the edge of the eyelids can cast shadows and obscure the view of fundus imaging, which hinders reliable interpretation and subsequent screening of fundus diseases. Despite its limitations, there are currently no effective methods or datasets available for removing eyelash artifacts from UWF fundus images. This research aims to develop an effective approach for eyelash artifact removal and thus improve the visual quality of UWF fundus images for accurate analysis and diagnosis. Methods: To address this issue, we first constructed two UWF fundus datasets: the paired synthetic eyelashes (PSE) dataset and the unpaired real eyelashes (uPRE) dataset. Then we proposed a deep learning architecture called Joint Conditional Generative Adversarial Networks (JcGAN) to remove eyelash artifacts from UWF fundus images. JcGAN employs a shared generator with two discriminators for joint learning of both real and synthetic eyelash artifacts. Furthermore, we designed a background refinement module that refines background information and is trained with the generator in an end-to-end manner. Results: Experimental results on both PSE and uPRE datasets demonstrate the superiority of the proposed JcGAN over several state-of-the-art deep learning approaches. Compared with the best existing method, JcGAN improves PSNR and SSIM by 4.82% and 0.23%, respectively. In addition, we also verified that eyelash artifact removal via JcGAN could significantly improve vessel segmentation performance in UWF fundus images. Assessment via vessel segmentation illustrates that the sensitivity, Dice coefficient and area under curve (AUC) of ResU-Net have respectively increased by 3.64%, 1.54%, and 1.43% after eyelash artifact removal using JcGAN. Conclusion: The proposed JcGAN effectively removes eyelash artifacts in UWF images, resulting in improved visibility of retinal vessels. Our method can facilitate better processing and analysis of retinal vessels and has the potential to improve diagnostic outcomes.

Immediate subretinal fluid displacement from the buoyant force of a small gas bubble in pneumatic retinopexy: Insights into the potential mechanism of retinal displacement following retinal detachment repair.

To demonstrate how a small gas bubble injected into the vitreous cavity in pneumatic retinopexy (PnR) for rhegmatogenous retinal detachment (RRD) causes immediate displacement of subretinal fluid and to gain insights into the potential mechanism of retinal displacement.Methods: Three patients with RRD who underwent PnR were enrolled and prospectively followed. All patients underwent ultra-widefield fundus photography at baseline and at 1-2 minutes after intravitreal gas injection. In all cases, the ultra-widefield fundus photograph demonstrated immediate displacement of subretinal fluid, suggesting that the buoyant force applied to the retina by the bubble was responsible for the displacement of subretinal fluid. The results were extrapolated to determine the buoyant force applied by a small and large gas bubble as in PnR and pars plana vitrectomy (PPV). We determined that the buoyant force applied with a larger bubble in PPV was substantially greater and this may lead to retinal displacement. Intravitreal gas applies significant buoyant force to the detached retina and subretinal fluid which leads to substantial and rapid displacement of subretinal fluid. Understanding the impact of the buoyant force of the gas bubble on the detached retina can provide insight into possible mechanisms of retinal displacement.

Color fundus imaging in retinopathy of prematurity screening

Advent of pediatric handheld fundus cameras like RetCam, 3netra Forus, and Phoenix ICON pediatric retinal camera has aided in effective screening of retinopathy of prematurity (ROP), especially in countries with limited number of trained specialists. Recent advent of various smartphone-based cameras has made pediatric fundus photography furthermore affordable and portable. Future advances like ultra-wide field fundus cameras, trans-pars-planar illumination pediatric fundus camera, artificial intelligence, deep learning algorithm, and handheld SS-OCTA can help in more accurate imaging and documentation. This article summarizes the features of existing and upcoming imaging modalities in detail, including their features, advantages, challenges, and effectiveness, which can help in implementation of telescreening as a standard screening protocol for ROP across developing as well as developed countries.

Automatic Detection of Peripheral Retinal Lesions From Ultrawide-Field Fundus Images Using Deep Learning.

To establish a multilabel-based deep learning (DL) algorithm for automatic detection and categorization of clinically significant peripheral retinal lesions using ultrawide-field fundus images. A total of 5958 ultrawide-field fundus images from 3740 patients were randomly split into a training set, validation set, and test set. A multilabel classifier was developed to detect rhegmatogenous retinal detachment, cystic retinal tuft, lattice degeneration, and retinal breaks. Referral decision was automatically generated based on the results of each disease class. t -distributed stochastic neighbor embedding heatmaps were used to visualize the features extracted by the neural networks. Gradient-weighted class activation mapping and guided backpropagation heatmaps were generated to investigate the image locations for decision-making by the DL models. The performance of the classifier(s) was evaluated by sensitivity, specificity, accuracy, F 1 score, area under receiver operating characteristic curve (AUROC) with 95% CI, and area under the precision-recall curve. In the test set, all categories achieved a sensitivity of 0.836-0.918, a specificity of 0.858-0.989, an accuracy of 0.854-0.977, an F 1 score of 0.400-0.931, an AUROC of 0.9205-0.9882, and an area under the precision-recall curve of 0.6723-0.9745. The referral decisions achieved an AUROC of 0.9758 (95% CI= 0.9648-0.9869). The multilabel classifier had significantly better performance in cystic retinal tuft detection than the binary classifier (AUROC= 0.9781 vs 0.6112, P < 0.001). The model showed comparable performance with human experts. This new DL model of a multilabel classifier is capable of automatic, accurate, and early detection of clinically significant peripheral retinal lesions with various sample sizes. It can be applied in peripheral retinal screening in clinics.

Optometrists' attitudes toward using OCT angiography lag behind other retinal imaging types.

While optometrists' attitudes toward established retinal imaging types are generally positive, they are unknown for optical coherence tomography angiography (OCTA). We performed a cross-sectional survey to estimate attitudes toward OCTA and identify clinician and/or practice characteristics that influence them. A paper-based survey was mailed to 252 randomly selected optometrists in Australia. Five-point Likert-scale items from a previous survey assessing attitudes toward new technology were included to probe respondent characteristics and attitudes toward retinal imaging. Performance expectancy attitudes toward OCTA were elicited by the statement 'I believe OCTA is useful in daily practice'. Mean scores out of five (mean [SD]) were rounded and mapped to appropriate descriptive statements. The response rate was 47% (118/252). The mean (SD) age of respondents was 44.0 (13.8) years and 50.8% (60/118) were female. Optometrists had 19.9 (14.0) years of clinical experience and 66.9% (79/118) worked at independent practices. In total, 8.5% (10/118) of respondents used OCTA to provide clinical care. Optometrists agreed that optical coherence tomography (OCT), colour fundus imaging, ultra-wide field imaging and fundus autofluorescence (mean scores 3.6-4.7 out of 5) were useful in daily practice but felt neutral about whether OCTA was useful (3.4 [0.8]). Optometrists believed that OCTA was less enjoyable to use (p < 0.0001), less endorsed by peers (p < 0.0001) and felt less confident that they had the knowledge to interpret OCTA (p < 0.0001) compared to other retinal imaging types. Optometrists are undecided on whether OCTA is useful in daily practice and had lower expectations that using OCTA would confer job performance benefits compared to other retinal imaging types. Further work is needed to advocate the benefits of using OCTA across the profession.

Ultra-Widefield Imaging of Presumed Vitreous Base-Associated Vasculopathy: Assessment of Peripheral Retinal Hemorrhages and Microaneurysms.

Purpose: To describe cases of asymptomatic peripheral retinal hemorrhage attributed to presumed vitreous base traction seen on ultra-widefield (UWF) imaging. Methods: This retrospective consecutive series comprised asymptomatic patients with peripheral retinal hemorrhages, microaneurysms, or both. Imaging included UWF fundus photography, fundus autofluorescence, fluorescein angiography (FA), optical coherence tomography (OCT), or a combination. Results: The series included 9 adult patients. The findings were observed on a routine eye examination or as an incidental finding in the contralateral eye of patients presenting with a retinal break or detachment. On UWF imaging, the distinguishing features of the peripheral retinal hemorrhages and microaneurysms presumably caused by vitreous base traction were their pinpoint shape and location at the vitreous base, in particular in the far temporal and superior retinal periphery. UWF FA showed punctate hyperfluorescent spots with no leakage. OCT showed signs of evolving posterior vitreous detachment. Management was limited to observation; with time, the microaneurysms were stable and the hemorrhages resolved. Conclusions: UWF imaging has led to the identification of presumed vitreous base vasculopathy. After a targeted workup is unrevealing, observation is appropriate.

InTraocular EMulsion of Silicone oil (ITEMS) grading system: an evidence-based expert-led consensus.

To propose the InTraocular EMulsion of Silicone oil (ITEMS) grading system for the assessment of silicone oil (SiO) emulsion, applicable in a routine clinical setting and validated through an expert-led consensus procedure. Seven experts on intraocular liquid tamponades, led by a facilitator, performed a literature review on the detection of SiO emulsion. Based on the proposed ideas, a questionnaire was developed and submitted to the experts on the methods to detect SiO emulsion and the items to grade. After two rounds of individual ranking using a nine-point scale and related discussion, the final grading system was developed including items that reached consensus (score ≥7 from ≥75% of members). The agreed ITEMS grading system includes the identification of SiO microbubbles and large SiO bubbles through slit lamp biomicroscopy, gonioscopy, fundus examination under mydriasis or ultra-widefield fundus photography. Moreover, macular and disc OCT are used to detect SiO-associated hyperreflective dots. An evidence-based expert-led consensus was conducted to develop grading system of SiO emulsion, allowing, for the first time, homogenous collection of data on SiO emulsion. This has the potential to improve our understanding of the role and clinical relevance of SiO emulsion allowing comparisons between different studies.

Is Tumour Thickness Measurement Required for MOLES Scoring of Melanocytic Choroidal Tumours?

Introduction: It can be challenging to distinguish between choroidal naevi and melanomas in the community setting, particularly without access to ultrasonography (US), required to measure the thickness of melanocytic choroidal tumours. We aimed to determine whether thickness measurement is required for MOLES scoring of melanocytic choroidal tumours. Methods: The dataset of a recent MOLES evaluation was reviewed. Patients were selected for the present study if their MOLES tumour size category was determined by tumour thickness measured with US. The largest basal tumour diameter and tumour thickness were then measured from ultra-widefield fundus images and optical coherence tomography (OCT) images, respectively. Results: The tumour size category was determined by tumour diameter in 203/222 (91.4%) with no influence of tumour thickness. The tumour thickness influenced the MOLES score in 19/222 (8.6%) patients. In 11/19 patients with OCT measurements of tumour thickness, the US measurement exceeded the OCT by more than 25% in 5 patients, more than 50% in 2 patients, and more than 75% in 1 patient. As a result, the revised tumour thickness based on OCT determined the size category in 4/216 (1.8%) patients. The ultra-widefield fundus images measurements increased the diameter score by 1 in 5 patients. As a result, the revised tumour thickness determined the size category in 4/216 (1.8%) patients. If both the revised diameter and thickness scores were considered, the MOLES score reduced in 4 patients. If both the diameter and thickness scores were considered, the MOLES score reduced in 5 and increased in 1. Only 0.94% (2/211) of melanocytic choroidal tumours assessed with MOLES when using Optos ultra-widefield fundus images diameter and OCT to measure tumour diameter and thickness, respectively, required a change in management from a reduction in MOLES score from 1 to 0. Discussion/Conclusion: This study suggests that the MOLES category for size is influenced more by the tumour diameter, if it can be measured accurately, than by the thickness. This study suggests ignoring tumour thickness if this cannot be measured accurately with OCT, unless the tumour has a mushroom shape.

Ultra-Widefield OCT Angiography.

Optical Coherence Tomography Angiography (OCTA), a functional extension of OCT, has the potential to replace most invasive fluorescein angiography (FA) exams in ophthalmology. So far, OCTA's field of view is however still lacking behind fluorescence fundus photography techniques. This is problematic, because many retinal diseases manifest at an early stage by changes of the peripheral retinal capillary network. It is therefore desirable to expand OCTA's field of view to match that of ultra-widefield fundus cameras. We present a custom developed clinical high-speed swept-source OCT (SS-OCT) system operating at an acquisition rate 8-16 times faster than today's state-of-the-art commercially available OCTA devices. Its speed allows us to capture ultra-wide fields of view of up to 90 degrees with an unprecedented sampling density and hence extraordinary resolution by merging two single shot scans with 60 degrees in diameter. To further enhance the visual appearance of the angiograms, we developed for the first time a three-dimensional deep learning based algorithm for denoising volumetric OCTA data sets. We showcase its imaging performance and clinical usability by presenting images of patients suffering from diabetic retinopathy.

Associations and Prognostic Significance of Fluctuations in Diabetic Retinopathy Severity in Eyes Treated for Diabetic Macular Edema

Introduction: The aim of our study was to investigate factors associated with diabetic retinopathy (DR) severity fluctuations in patients undergoing intravitreal injections for diabetic macular edema and to explore risk factors for proliferative DR (PDR). Methods: We graded ultra-widefield fundus photography imaging at each visit using the Early Treatment Diabetic Retinopathy Study Severity Scale (DRSS). We calculated the deviation from the mode (DM) of DRSS values as a proxy of DR severity fluctuations, and we analyzed its clinical associations with linear models. We computed risk factors for PDR with Cox hazard models. We included the DRSS area-under-the-curve (AUC) of DRSS scores as a covariate in all analyses. Results: We included 111 eyes with a median follow-up of 44 months. Higher DRSS-AUC values (β = +0.03 DRSS DM for unitary DRSS/month increase, p = 0.01) and a higher number of anti-VEGF injections (β = +0.07 DRSS DM for injection, p = 0.045) were associated with wider DR severity fluctuations. Higher DRSS-AUC values (HR = 1.45 for unitary DRSS/month increase, p = 0.001) and wider DR severity fluctuations (HR = 22.35 4th quartile vs. 1st–3rd quartile of DRSS DM, p = 0.01) were risk factors for PDR. Conclusion: Patients with larger DR variability in response to intravitreal injections may be at higher risk of DR progression. We advocate attentive follow-up in these patients to recognize PDR early.

Early recognition of CLN3 disease facilitated by visual electrophysiology and multimodal imaging

BackgroundNeuronal ceroid lipofuscinosis is a group of neurodegenerative disorders with varying visual dysfunction. CLN3 is a subtype which commonly presents with visual decline. Visual symptomatology can be indistinct making early diagnosis difficult. This study reports ocular biomarkers of CLN3 patients to assist clinicians in early diagnosis, disease monitoring, and future therapy.MethodsRetrospective review of 5 confirmed CLN3 patients in our eye clinic. Best corrected visual acuity (BCVA), electroretinogram (ERG), ultra-widefield (UWF) fundus photography and fundus autofluorescence (FAF), and optical coherence tomography (OCT) studies were undertaken.ResultsFive unrelated children, 4 females and 1 male, with median age of 6.2 years (4.6–11.7) at first assessment were investigated at the clinic from 2016 to 2021. Four homozygous and one heterozygous pathogenic CLN3 variants were found. Best corrected visual acuities (BCVAs) ranged from 0.18 to 0.88 logMAR at first presentation. Electronegative ERGs were identified in all patients. Bull’s eye maculopathies found in all patients. Hyper-autofluorescence ring surrounding hypo-autofluorescence fovea on FAF was found. Foveal ellipsoid zone (EZ) disruptions were found in all patients with additional inner and outer retinal microcystic changes in one patient. Neurological problems noted included autism, anxiety, motor dyspraxia, behavioural issue, and psychomotor regression.ConclusionsCLN3 patients presented at median age 6.2 years with visual decline. Early onset maculopathy with an electronegative ERG and variable cognitive and motor decline should prompt further investigations including neuropaediatric evaluation and genetic assessment for CLN3 disease. The structural parameters such as EZ and FAF will facilitate ocular monitoring.

A new ultra-wide-field fundus dataset to diabetic retinopathy grading using hybrid preprocessing methods

Diabetic retinopathy(DR) is a common early diabetic complication and one of the main causes of blindness. In clinical diagnosis and treatment, regular screening with fundus imaging is an effective way to prevent the development of DR. However, the regular fundus images used in most DR screening work have a small imaging range, narrow field of vision, and can not contain more complete lesion information, which leads to less ideal automatic DR grading results. In order to improve the accuracy of DR grading, we establish a dataset containing 101 ultra-wide-field(UWF) DR fundus images and propose a deep learning(DL) automatic classification method based on a new preprocessing method. The emerging UWF fundus images have the advantages of a large imaging range and wide field of vision and contain more information about the lesions. In data preprocessing, we design a data denoising method for UWF images and use data enhancement methods to improve their contrast and brightness to improve the classification effect. In order to verify the efficiency of our dataset and the effectiveness of our preprocessing method, we design a series of experiments including a variety of DL classification models. The experimental results show that we can achieve high classification accuracy by using only the backbone model. The most basic ResNet50 model reaches an average of classification accuracy(ACA) 0.66, Macro F1 0.6559, and Kappa 0.58. The best-performing Swin-S model reaches ACA 0.72, Macro F1 0.7018, and Kappa 0.65. DR grading using UWF images can achieve higher accuracy and efficiency, which has practical significance and value in clinical applications.

Comparison of 50° handheld fundus camera versus ultra-widefield table-top fundus camera for diabetic retinopathy detection and grading.

To compare the performance of a handheld fundus camera with standard 50° visual field to ultra-widefield (UWF) table-top fundus camera in diabetic retinopathy (DR) detection and grading. Patients affected by diabetes mellitus and referred to our diabetic retinopathy clinic were enroled and underwent fundus photography in mydriasis. All photos were taken using the ultra-widefield table-top fundus camera Zeiss Clarus™ 500 (four fields per eye) and the Optomed Aurora® handheld fundus camera (3 fields per eye). The following parameters were analysed: the gradability of the images, the grade of DR, and diabetic maculopathy (DM), the presence of hypertensive retinopathy (HR) and the presence of other ocular diseases. We enroled 759 eyes of 384 diabetic patients and analysed 5313 fundus photos. The handheld fundus camera obtained a sensitivity of 84.2% and specificity of 95.4% for referable cases. Moreover, it obtained, compared to UWF, an almost perfect agreement with linear weighting for DR, DM and HR (k = 0.877, k = 0.854, and k = 0.961, respectively). The lowest sensitivity was achieved for proliferative DR (58.7% sensitivity, 100% specificity). Optomed Aurora® handheld fundus camera imaging showed a strong agreement compared to UWF in grading DR, considering all DR and DM grades, in mydriasis. However, the use of UWF imaging increases the detection of referable eyes.

New technology using crystalline lens autofluorescence for presbyopia and cataract grading.

To investigate whether fundus autofluorescence (FAF) obtained using an ultra-wide field (UWF) fundus camera with an artificial opacity pattern can grade the degree of presbyopia and nuclear cataract. Sixty eyes of 30 patients were enrolled in this prospective diagnostic study. The nuclear cataract (nuclear color/opalescence (NC/NO)) was graded according to the Lens Opacity Classification System III. The monocular near point of accommodation (NPA) was measured in eyes with NC3/NO3 or less. The mean gray value difference between the central 8 artificial opacity lesions and peripheral 8 artificial opacity lesions in the retinal AF was measured. The correlation between the mean gray value difference, NPA, and nuclear cataract grade was analyzed. The mean nuclear cataract grade of 60 eyes was 3.2 ± 1.6 and mean NPA of 37 eyes was 45.3 ± 16.1 cm. The mean gray value differences increased with increasing nuclear cataract grade (eyes with NC/NO grade 1, 53.3 ± 11.4; 2, 78.3 ± 13.6; 3, 95.2 ± 12.2; 4, 101.6 ± 11.9; 5, 109.0 ± 22.9; and 6, 121.1 ± 12.0; p < 0.001). The mean gray value difference was positively correlated with both the monocular NPA (R2 = 0.637; β coefficient = 1.009; 95% CI, 0.748 to 1.271; p < 0.001) and nuclear cataract grade (R2 = 0.661; β coefficient = 12.437; 95% CI, 10.097 to 14.778; p < 0.001). The FAF camera with an artificial opacity pattern attached can be used to effectively diagnose the degree of presbyopia and nuclear cataract.

Estimation of Visual Function Using Deep Learning From Ultra-Widefield Fundus Images of Eyes With Retinitis Pigmentosa

There is no widespread effective treatment to halt the progression of retinitis pigmentosa. Consequently, adequate assessment and estimation of residual visual function are important clinically. To examine whether deep learning can accurately estimate the visual function of patients with retinitis pigmentosa by using ultra-widefield fundus images obtained on concurrent visits. Data for this multicenter, retrospective, cross-sectional study were collected between January 1, 2012, and December 31, 2018. This study included 695 consecutive patients with retinitis pigmentosa who were examined at 5 institutions. Each of the 3 types of input images-ultra-widefield pseudocolor images, ultra-widefield fundus autofluorescence images, and both ultra-widefield pseudocolor and fundus autofluorescence images-was paired with 1 of the 31 types of ensemble models constructed from 5 deep learning models (Visual Geometry Group-16, Residual Network-50, InceptionV3, DenseNet121, and EfficientNetB0). We used 848, 212, and 214 images for the training, validation, and testing data, respectively. All data from 1 institution were used for the independent testing data. Data analysis was performed from June 7, 2021, to December 5, 2022. The mean deviation on the Humphrey field analyzer, central retinal sensitivity, and best-corrected visual acuity were estimated. The image type-ensemble model combination that yielded the smallest mean absolute error was defined as the model with the best estimation accuracy. After removal of the bias of including both eyes with the generalized linear mixed model, correlations between the actual values of the testing data and the estimated values by the best accuracy model were examined by calculating standardized regression coefficients and P values. The study included 1274 eyes of 695 patients. A total of 385 patients were female (55.4%), and the mean (SD) age was 53.9 (17.2) years. Among the 3 types of images, the model using ultra-widefield fundus autofluorescence images alone provided the best estimation accuracy for mean deviation, central sensitivity, and visual acuity. Standardized regression coefficients were 0.684 (95% CI, 0.567-0.802) for the mean deviation estimation, 0.697 (95% CI, 0.590-0.804) for the central sensitivity estimation, and 0.309 (95% CI, 0.187-0.430) for the visual acuity estimation (all P < .001). Results of this study suggest that the visual function estimation in patients with retinitis pigmentosa from ultra-widefield fundus autofluorescence images using deep learning might help assess disease progression objectively. Findings also suggest that deep learning models might monitor the progression of retinitis pigmentosa efficiently during follow-up.

Iatrogenic ophthalmic artery occlusion after platelet-rich plasma dermal filler documented with ultra-widefield imaging.

To report a case of iatrogenic ophthalmic artery occlusion (OAO) secondary to platelet-rich plasma (PRP) dermal filler injection for facial rejuvenation documented with ultra-widefield imaging. Case report. A 45-year-old woman developed a sudden and painful vision loss in the left eye (LE) after a dermal filler injection of PRP in the left glabellar region. She immediately received intravenous corticosteroids with no improvements. Two weeks later a complete ophthalmological examination including visual acuity (VA), fundus examination, ultra-widefield fundus autofluorescence and fluorescein angiography, and optical coherence tomography was performed. A diagnosis of iatrogenic OAO in the LE with profound ocular ischemia was made and VA remained no light perception. Monthly follow-up visits were scheduled to ascertain the onset of any ocular complication. Dermal filler injections of PRP can lead to rare but devastating side effects with permanent visual loss. Considering that there is currently no validated treatment strategy, prevention could be the real key of iatrogenic OAO management.

Deep Learning-based Prediction of Axial Length Using Ultra-widefield Fundus Photography.

To develop a deep learning model that can predict the axial lengths of eyes using ultra-widefield (UWF) fundus photography. We retrospectively enrolled patients who visited the ophthalmology clinic at the Seoul National University Hospital between September 2018 and December 2021. Patients with axial length measurements and UWF images taken within 3 months of axial length measurement were included in the study. The dataset was divided into a development set and a test set at an 8:2 ratio while maintaining an equal distribution of axial lengths (stratified splitting with binning). We used transfer learning-based on EfficientNet B3 to develop the model. We evaluated the model's performance using mean absolute error (MAE), R-squared (R2), and 95% confidence intervals (CIs). We used vanilla gradient saliency maps to illustrate the regions predominantly used by convolutional neural network. In total, 8,657 UWF retinal fundus images from 3,829 patients (mean age, 63.98 ±15.25 years) were included in the study. The deep learning model predicted the axial lengths of the test dataset with MAE and R2 values of 0.744 mm (95% CI, 0.709-0.779 mm) and 0.815 (95% CI, 0.785-0.840), respectively. The model's accuracy was 73.7%, 95.9%, and 99.2% in prediction, with error margins of ±1.0, ±2.0, and ±3.0 mm, respectively. We developed a deep learning-based model for predicting the axial length from UWF images with good performance.