Conventional Fundus Research Articles

Effect of Ocular Perfusion Pressure on Incidence of Diabetic Retinopathy in Type 2 Diabetes: A Two-Year Prospective Study.

To investigate the association between mean ocular perfusion pressure (MOPP), estimated cerebrospinal fluid pressure (CSFP), and changes in diabetic retinopathy (DR) in a Southern Chinese population with type 2 diabetes (T2DM). A total of 1224 subjects from the Guangzhou Diabetic Eye Study were enrolled. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and intraocular pressure (IOP) were measured. MOPP was calculated with the formula: MOPP = 2/3 [DBP + 1/3 (SBP - DBP)] - IOP. CSFP was calculated using the formula: CSFP = 0.44 × body mass index (kg/m2) + 0.16 × DBP - 0.18 × age (years) - 1.91. Incidence, progression, and regression of DR were graded based on seven-field 45° conventional fundus photographs at baseline and during two-year follow-up examinations according to the United Kingdom National Diabetic Eye Screening Program guidelines. Higher MOPP was associated with DR incidence in the multivariate model (per 1 mm Hg increase: relative risk, 1.05; 95% confidence interval, 1.01-1.09; P = 0.02) and was not associated with DR development and DR regression in two-year follow-up of T2DM patients. However, CSFP was not associated with DR changes (incidence, progression, or regression). The higher MOPP is an independent risk factor for DR incidence among T2DM patients in a Southern Chinese cohort. Monitoring MOPP and managing blood pressure can be part of a comprehensive approach to prevent or delay the onset of DR in T2DM patients. MOPP might be an indicator for the detection of DR incidence.

Automatic Segmentation of Hemorrhages in the Ultra-wide Field Retina: Multi-scale Attention Subtraction Networks and An Ultra-wide Field Retinal Hemorrhage Dataset.

Ultra-wide field (UWF) retinal imaging can improve the detection rate of retinal hemorrhage as compared with conventional fundus images. However, hemorrhages in UWF retinal images can also become smaller and more widely distributed, which can be time consuming and labor intensive. With the development of computer technology, automatic segmentation techniques can assist physicians in diagnosis. However, the lack of publicly available UWF retinal hemorrhage segmentation datasets has limited the development of automatic hemorrhage segmentation techniques in UWF retinal images. We present a large-scale high-quality UWF retinal hemorrhage segmentation dataset, named UWF-RHS Dataset, for public use. To the best of our knowledge, we are the first team to make the UWF retinal hemorrhage segmentation dataset publicly available. In addition, we propose a multi-scale attention subtraction network (MASNet) for UWF retinal hemorrhage segmentation. Specifically, highly focused lesion features are extracted by using the proposed multi-scale attention subtraction (MAS) module at the progress of the skip-connection. Several comparative experiments and ablation experiments were performed at the UWF-RHS Dataset, and all experiments state that our proposed method is effective in diagnosing retinal hemorrhages with state-of-the-art results. The proposed UWF-RHS dataset and MASNet will greatly facilitate the development of automated segmentation techniques for UWF retinal hemorrhages. Links to the UWF-RHS dataset and the MASNet model code are available fromhttps://github.com/wurenkai/UWF-RHS-Dataset-and-MASNet.

Quantitative comparison of automated OCT and conventional FAF-based geographic atrophy measurements in the phase 3 OAKS/DERBY trials

With the approval of the first two substances for the treatment of geographic atrophy (GA) secondary to age-related macular degeneration (AMD), a standardized monitoring of patients treated with complement inhibitors in clinical practice is needed. Optical coherence tomography (OCT) provides high-resolution access to the retinal pigment epithelium (RPE) and neurosensory layers, such as the ellipsoid zone (EZ), which further enhances the understanding of disease progression and therapeutic effects in GA compared to conventional fundus autofluorescence (FAF). In addition, artificial intelligence-based methodology allows the identification and quantification of GA-related pathology on OCT in an objective and standardized manner. The purpose of this study was to comprehensively evaluate automated OCT monitoring for GA compared to reading center-based manual FAF measurements in the largest successful phase 3 clinical trial data of complement inhibitor therapy to date. Automated OCT analysis of RPE loss showed a high and consistent correlation to manual GA measurements on conventional FAF. EZ loss on OCT was generally larger than areas of RPE loss, supporting the hypothesis that EZ loss exceeds underlying RPE loss as a fundamental pathophysiology in GA progression. Automated OCT analysis is well suited to monitor disease progression in GA patients treated in clinical practice and clinical trials.

Methodological Approaches to Optical Disc and Optical Cup Segmentation: A Critical Assessment

A progressive optic nerve condition called glaucoma causes irreversible eyesight loss. To diagnose retinal diseases, retinal fundus imaging has been used in recent years. Analyzing these images effectively requires pinpointing the areas of interest, which can be tricky, due to the anatomy and vascular patterns in fundus images. Different image segmentation techniques are used to extract the area of interest from the fundus images. This paper explores the various segmentation methodologies, emphasizing conventional and modern retinal fundus image segmentation approaches. Evaluation measures such as the Disc damage likelihood scale, Inferior superior temporal region, Dice similarity coefficient, Jaccard index, Sensitivity and Specificity are used to measure the effectiveness of segmentation algorithms, which detect small structural changes that indicate glaucomatous damage. Furthermore, this paper also provides a detailed analysis of deep learning algorithms used for optic cup and optic disc segmentation. This detailed analysis demonstrates that accurate disc and cup segmentation remains a significant challenge and suggests that effective segmentation strategies and deep learning approaches are required for vast and complex datasets.

A Deep Learning Approach for Accurate Discrimination Between Optic Disc Drusen and Papilledema on Fundus Photographs.

Optic disc drusen (ODD) represent an important differential diagnosis of papilledema caused by intracranial hypertension, but their distinction may be difficult in clinical practice. The aim of this study was to train, validate, and test a dedicated deep learning system (DLS) for binary classification of ODD vs papilledema (including various subgroups within each category), on conventional mydriatic digital ocular fundus photographs collected in a large international multiethnic population. This retrospective study included 4,508 color fundus images in 2,180 patients from 30 neuro-ophthalmology centers (19 countries) participating in the Brain and Optic Nerve Study with Artificial Intelligence (BONSAI) Group. For training and internal validation, we used 857 ODD images and 3,230 papilledema images, in 1,959 patients. External testing was performed on an independent data set (221 patients), including 207 images with ODD (96 visible and 111 buried), provided by 3 centers of the Optic Disc Drusen Studies Consortium, and 214 images of papilledema (92 mild-to-moderate and 122 severe) from a previously validated study. The DLS could accurately distinguish between all ODD and papilledema (all severities included): area under the receiver operating characteristic curve (AUC) 0.97 (95% confidence interval [CI], 0.96-0.98), accuracy 90.5% (95% CI, 88.0%-92.9%), sensitivity 86.0% (95% CI, 82.1%-90.1%), and specificity 94.9% (95% CI, 92.3%-97.6%). The performance of the DLS remained high for discrimination of buried ODD from mild-to-moderate papilledema: AUC 0.93 (95% CI, 0.90-0.96), accuracy 84.2% (95% CI, 80.2%-88.6%), sensitivity 78.4% (95% CI, 72.2%-84.7%), and specificity 91.3% (95% CI, 87.0%-96.4%). A dedicated DLS can accurately distinguish between ODD and papilledema caused by intracranial hypertension, even when considering buried ODD vs mild-to-moderate papilledema.

A Miniaturized Large-Field Fundus Optical System Based on Aspheric Imaging and Non-Coaxial Illumination

Many diseases produce pathological changes in the fundus; analyzing the retinopathy of the fundus could help diagnose diseases in time. A fundus camera is a medical imaging device that specializes in taking fundus images to diagnose hypertension, coronary heart disease, diabetes, and others. The fundus optical imaging system is the core part of it. Nevertheless, the conventional fundus optical imaging system is large and not suitable for mobile examination and follow-up use. So, it has not been widely used in medical institutions. In this paper, a miniaturized fundus optical imaging system based on aspheric technology and non-coaxial illumination is proposed. The length of the imaging system is only 34.6 mm, the field of view is 50°, and the MTF curve is greater than 0.2 at 100 lp/mm, which can resolve the structure of 5 um. The illumination system adopts a non-coaxial annular array illumination structure to avoid occlusion of the imaging system. Our study effectively tackles the pressing predicament of fundus optical system miniaturization. This innovative paradigm harbors the potential to revolutionize fundus image data acquisition, propelling the field of fundus diagnosis forward and efficiently catering to crucial applications, improving the versatility of fundus examination, and providing technical support for the intelligent diagnosis system.

Can ultra-widefield optical coherence tomography angiography replace traditional fundus angiography

Fundus imaging plays a pivotal role in diagnosing retinal and choroidal diseases. Optical coherence tomography angiography (OCTA), by capturing signals to reconstruct vascular structures, offers a clear depiction of retinal vasculature with notable advantages such as rapid scanning and non-invasiveness. Although OCTA, due to its underlying principles, cannot dynamically assess vascular function, exploring its future applications and potential to eventually replace traditional fundus angiography remains a key focus in the medical community. OCTA provides multiple parameters that conventional fundus angiography cannot obtain. With the expanding coverage area of OCTA scans and improvements in artifact elimination, the detection rate of various retinal and choroidal diseases has significantly increased, making the widespread clinical application of OCTA an inevitable trend. Although ultra-widefield OCTA cannot yet fully replace angiography in clinical practice, with continued clinical practice, expanded clinical research, and ongoing technological innovation, OCTA is expected to gradually replace fundus angiography in the future.

Advances in Structural and Functional Retinal Imaging and Biomarkers for Early Detection of Diabetic Retinopathy.

Diabetic retinopathy (DR), a vision-threatening microvascular complication of diabetes mellitus (DM), is a leading cause of blindness worldwide that requires early detection and intervention. However, diagnosing DR early remains challenging due to the subtle nature of initial pathological changes. This review explores developments in multimodal imaging and functional tests for early DR detection. Where conventional color fundus photography is limited in the field of view and resolution, advanced quantitative analysis of retinal vessel traits such as retinal microvascular caliber, tortuosity, and fractal dimension (FD) can provide additional prognostic value. Optical coherence tomography (OCT) has also emerged as a reliable structural imaging tool for assessing retinal and choroidal neurodegenerative changes, which show potential as early DR biomarkers. Optical coherence tomography angiography (OCTA) enables the evaluation of vascular perfusion and the contours of the foveal avascular zone (FAZ), providing valuable insights into early retinal and choroidal vascular changes. Functional tests, including multifocal electroretinography (mfERG), visual evoked potential (VEP), multifocal pupillographic objective perimetry (mfPOP), microperimetry, and contrast sensitivity (CS), offer complementary data on early functional deficits in DR. More importantly, combining structural and functional imaging data may facilitate earlier detection of DR and targeted management strategies based on disease progression. Artificial intelligence (AI) techniques show promise for automated lesion detection, risk stratification, and biomarker discovery from various imaging data. Additionally, hematological parameters, such as neutrophil-lymphocyte ratio (NLR) and neutrophil extracellular traps (NETs), may be useful in predicting DR risk and progression. Although current methods can detect early DR, there is still a need for further research and development of reliable, cost-effective methods for large-scale screening and monitoring of individuals with DM.

Detection of capillary abnormalities in early diabetic retinopathy using scanning laser ophthalmoscopy and optical coherence tomography combined with adaptive optics

This study tested if a high-resolution, multi-modal, multi-scale retinal imaging instrument can provide novel information about structural abnormalities in vivo. The study examined 11 patients with very mild to moderate non-proliferative diabetic retinopathy (NPDR) and 10 healthy subjects using fundus photography, optical coherence tomography (OCT), OCT angiography (OCTA), adaptive optics scanning laser ophthalmoscopy (AO-SLO), adaptive optics OCT and OCTA (AO-OCT(A)). Of 21 eyes of 11 patients, 11 had very mild NPDR, 8 had mild NPDR, 2 had moderate NPDR, and 1 had no retinopathy. Using AO-SLO, capillary looping, inflections and dilations were detected in 8 patients with very mild or mild NPDR, and microaneurysms containing hyperreflective granular elements were visible in 9 patients with mild or moderate NPDR. Most of the abnormalities were seen to be perfused in the corresponding OCTA scans while a few capillary loops appeared to be occluded or perfused at a non-detectable flow rate, possibly because of hypoperfusion. In one patient with moderate NPDR, non-perfused capillaries, also called ghost vessels, were identified by alignment of corresponding en face AO-OCT and AO-OCTA images. The combination of multiple non-invasive imaging methods could identify prominent microscopic abnormalities in diabetic retinopathy earlier and more detailed than conventional fundus imaging devices.

Posterior segment optical coherence tomography: A diagnostic aid in posterior uveitis

Background: Patients with posterior uveitis can develop vision-threatening complications. Optical coherence tomography (OCT) plays an important role in the diagnosis and management of these complications. Aims: The objectives of this study were to describe different retinal morphological characteristics presenting on OCT in patients with posterior uveitis, with an aim to facilitate early diagnosis to initiate specific treatment and also to observe the response to treatment. Materials and Methods: A cross-sectional prospective non-randomized study was undertaken at a tertiary eye care hospital. The study included 30 eyes of 30 patients with posterior uveitis between the period of May 2016 and May 2018. All subjects underwent a set of systemic investigations for etiological diagnosis and complete ophthalmic examination, including OCT (Zeiss Cirrus machine (model number 5000)] imaging). Data were collected and analyzed. A P value of <0.05 was considered statistically significant. Results: The mean patient age was 33.1 ± 4.2 years. Infectious etiology was diagnosed in 13 patients (43.33%) of which toxoplasma chorioretinitis (36.66%) was the most common infection. Among 17 patients with noninfectious uveitis (56.66%), Vogt-Koyanagi-Harada (VKH) disease and multifocal choroiditis (16.67% each) were the most common causes. RPE-Bruch’s membrane abnormalities were seen in 96.67% of patients. Subretinal detachment (26.67%) was the most common type of macular edema on OCT. Significant differences in central foveal thickness posttreatment were noted in cases with VKH syndrome and toxoplasma retinochoroiditis (P < 0.05). Conclusion: OCT is a useful tool complementary to conventional fundus photography and fluorescein angiography in patients with posterior uveitis.

Effectiveness of Conventional Digital Fundus Photography-Based Teleretinal Screening for Diabetic Retinopathy and Diabetic Macular Edema: A Report by the American Academy of Ophthalmology

This American Academy of Ophthalmology Ophthalmic Technology Assessment aims to assess the effectiveness of conventional teleretinal screening (TS) in detecting diabetic retinopathy (DR) and diabetic macular edema (DME). A literature search of the PubMed database was conducted most recently in July 2023 to identify data published between 2006 and 2023 on any of the following elements related to TS effectiveness: (1) the accuracy of TS in detecting DR or DME compared with traditional ophthalmic screening with dilated fundus examination or 7-standard field Early Treatment Diabetic Retinopathy Study photography, (2) the impact of TS on DR screening compliance rates or other patient behaviors, and (3) cost-effectiveness and patient satisfaction of TS compared with traditional DR screening. Identified studies then were rated based on the Oxford Centre for Evidence-Based Medicine grading system. Eight level I studies, 14 level II studies, and 2 level III studies were identified in total. Although cross-study comparison is challenging because of differences in reference standards and grading methods, TS demonstrated acceptable sensitivity and good specificity in detecting DR; moderate to good agreement between TS and reference-standard DR grading was observed. Performance of TS was not as robust in detecting DME, although the number of studies evaluating DME specifically was limited. Two level I studies, 5 level II studies, and 1 level III study supported that TS had a positive impact on overall DR screening compliance, even increasing it by more than 2-fold in one study. Studies assessing cost-effectiveness and patient satisfaction were not graded formally, but they generally showed that TS was cost-effective and preferred by patients over traditional surveillance. Conventional TS is an effective approach to DR screening not only for its accuracy in detecting referable-level disease, but also for improving screening compliance in a cost-effective manner that may be preferred by patients. Further research is needed to elucidate the ideal approach of TS that may involve integration of artificial intelligence or other imaging technologies in the future. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Graph-based multi-level feature fusion network for diabetic retinopathy grading using ultra-wide-field images

Diabetic Retinopathy (DR) grading plays an important role for early intervention in managing the disease. However, conventional grading methods predominantly rely on traditional color fundus photography, which may not fully capture crucial pathological information. In contrast, Ultra-wide-field (UWF) represents a novel non-contact and non-invasive modality, capable of generating images with an unprecedented field of view, ranging from 180∘ to 200∘. This offers distinct advantages over conventional color fundus photography for DR grading. Furthermore, given the subtle distinctions in imaging characteristics among adjacent levels of DR, the grading task becomes exceptionally challenging. Therefore, we propose a new multi-level feature fusion network based on the graph convolution to accomplish the DR grading task using UWF images. Specifically, we first use the progressive multi-level constraint module to iteratively refine features by imposing constraints on model features at different levels. This process serves to minimize the inter-individual distances within the intra-class feature space, ultimately reducing the overlap of feature spaces between adjacent classes. Subsequently, since features at different network levels exhibit distinct semantics and distributions, conventional aggregation techniques, such as splicing and summing, may introduce redundant information and noise interference. To address this issue, we introduce a graph-based feature fusion module, capable of capturing spatial relationships and effectively fusing multi-layered features. Experimental results evaluated on an in-house UWF dataset with 1234 images and a publicly available fundus dataset with 3662 images illustrate that the proposed approach outperforms the state-of-the-art methods and shows promise for DR grading, with a respective accuracy of 0.81 and 0.84.

Dehazing Algorithm for Enhancing Fundus Photographs Using Dark Channel and Bright Channel Prior

Purpose: We present a dehazing algorithm using dark channel prior (DCP) and bright channel prior (BCP) to enhance the quality of retinal images obtained through conventional fundus photography.Methods: A retrospective analysis was conducted on retinal images from patients who visited Gangnam Sacred Heart Hospital between January 2000 and September 2022. These images were captured using a digital fundus camera (KOWA Nonmyd 8S Fundus Camera, KOWA Company, Nagoya, Japan) without pupil dilation. We used two mathematical algorithms: DCP only and DCP and BCP combined. The original, DCP-processed, and DCP & BCP-processed images were compared. Fisher's exact test was used to identify significant quality improvements.Results: The DCP and the newly proposed DCP plus BCP algorithm effectively eliminated haze and enhanced the contrast of cataract images. Notably, DCP demonstrated limited improvements in fundus photographs from patients with small pupils, whereas the proposed DCP plus BCP method effectively revealed previously obscured retinal details and vessels. However, these methods exhibited limited performance in severe cataracts compared to the clear images obtained after surgery. The quality enhancement with the proposed method was significant in photographs of patients with cataracts (<i>p</i> = 0.032) and small pupils (<i>p</i> < 0.01).Conclusions: Our algorithm produced clearer images of blood vessels and optic disc structures, while significantly reducing artifacts in fundus images from patients with small pupils or cataracts. The proposed algorithm can provide visually enhanced images, potentially aiding physicians in the diagnosis of retinal diseases in patients with cataracts.

Poster Session II: Understanding scattering in high-resolution retinal imaging.

In conventional fundus images the contrast relates to scattering and absorption of light. Here, we report on modeling of high-resolution retinal imaging to understand the role of mitochondria light scattering in the ellipsoid of photoreceptors. We evaluate the directionality appearance of the photoreceptors, and its relation to drusen appearance in age-related macular degeneration (AMD). The refractive index contrast of the mitochondria collectively resembles that of micron-sized reflectors. Obliqueness of the scattering results in vignetting by the pupil that impacts on the brightness of the photoreceptors. Our hypothesis is that in early-stage AMD, drusen will perturb the photoreceptor alignment and thus cause oblique scattering and reduced brightness on the slope of the drusen in high-resolution retinal images. We model how this affects the observable photoreceptor mosaic both in the healthy eye and in eyes with AMD. The results confirm that the annular dark ring around drusen is likely caused by oblique scattering. We use a model eye to test the hypothesis with a reflective mask emulating the photoreceptor mosaic An SLM is used for amplitude modulation in the pupil and differential imaging allows for capturing off-axis images with an appearance like that of phase contrast images. Further refinement of the model eye may be a valuable way to test new imaging modalities prior to their implementation in the real eye.

Preserving polarization maintaining photons for enhanced contrast imaging of the retina.

The purpose of this study is to demonstrate the feasibility of using polarization maintaining photons for enhanced contrast imaging of the retina. Orthogonal-polarization control has been frequently used in conventional fundus imaging systems to minimize reflection artifacts. However, the orthogonal-polarization configuration also rejects the directly reflected photons, which preserve the polarization condition of incident light, from the superficial layer of the fundus, i.e., the retina, and thus reduce the contrast of retinal imaging. We report here a portable fundus camera which can simultaneously perform orthogonal-polarization control to reject back-reflected light from the ophthalmic lens and parallel-polarization control to preserve the backscattered light from the retina which partially maintains the polarization state of the incoming light. This portable device utilizes miniaturized indirect ophthalmoscopy illumination to achieve non-mydriatic imaging, with a snapshot field of view of 101° eye-angle (67° visual-angle). Comparative analysis of retinal images acquired with a traditional orthogonal-polarization fundus camera from both normal and diseased eyes was conducted to validate the usefulness of the proposed design. The parallel-polarization control for enhanced contrast in high dynamic range imaging has also been validated.

Assessment of early diabetic retinopathy severity using ultra-widefield Clarus versus conventional five-field and ultra-widefield Optos fundus imaging

To compare early diabetic retinopathy (DR) severity level and the abilities in detecting early DR lesions among conventional five-field, ultrawide-field (UWF) Optos, and UWF Clarus fundus imaging methods. This was a single-center, prospective, clinic-based, and comparative study. In total, 157 consecutive patients with diabetes mellitus were enrolled in this study. All patients underwent comprehensive ophthalmological examinations. Following mydriasis, each eye was examined with conventional five-field, UWF Optos, and UWF Clarus fundus imaging methods. The initial UWF images were overlaid with a template mask that obscured the retina, which created a five-field view from UWF images (covered UWF images). The covered UWF images were then graded, after which the template mask was removed, and the original UWF images were also evaluated. All images were graded using the International Clinical DR severity scale. DR grades were compared and analyzed by weighted kappa statistics among the three fundus imaging methods. In total, 157 consecutive patients with diabetes (302 eyes) were enrolled in this study. Weighted kappa statistics for agreement were 0.471 (five-field vs. covered Optos), 0.809 (five-field vs. covered Clarus), 0.396 (covered Optos vs. covered Clarus), 0.463 (five-field vs. Optos), 0.521 (five-field vs. Clarus 133°), 0.500 (five-field vs. Clarus 200°), 0.323 (Optos vs. Clarus 133°), and 0.349 (Optos vs. Clarus 200°). The area under curve of covered Clarus images was higher than that of conventional five-field images at three different thresholds. Compared with conventional five-field and Optos fundus imaging methods, Clarus fundus imaging methods exhibited excellent performance in assessing early DR severity. Thus, Clarus fundus imaging methods were superior for early detection of DR.

Self-FI: Self-Supervised Learning for Disease Diagnosis in Fundus Images.

Self-supervised learning has been successful in computer vision, and its application to medical imaging has shown great promise. This study proposes a novel self-supervised learning method for medical image classification, specifically targeting ultra-wide-field fundus images (UFI). The proposed method utilizes contrastive learning to pre-train a deep learning model and then fine-tune it with a small set of labeled images. This approach reduces the reliance on labeled data, which is often limited and costly to obtain, and has the potential to improve disease detection in UFI. This method employs two contrastive learning techniques, namely bi-lateral contrastive learning and multi-modality pre-training, to form positive pairs using the data correlation. Bi-lateral learning fuses multiple views of the same patient's images, and multi-modality pre-training leverages the complementary information between UFI and conventional fundus images (CFI) to form positive pairs. The results show that the proposed contrastive learning method achieves state-of-the-art performance with an area under the receiver operating characteristic curve (AUC) score of 86.96, outperforming other approaches. The findings suggest that self-supervised learning is a promising direction for medical image analysis, with potential applications in various clinical settings.

Comparison of Color Fundus Photography and Multicolor Fundus Imaging for Detection of Lesions in Diabetic Retinopathy and Retinal Vein Occlusion.

To evaluate the agreement between conventional fundus photography (CFP) and multicolor fundus imaging (MFI) for the detection of lesions of diabetic retinopathy (DR) and retinal vein occlusion (RVO). Cross-sectional analysis of eyes with DR or RVO who underwent CFP and MFI. All images were independently analyzed by two observers (O1 and O2), and the evaluated lesions were classified as "present" or "absent". Then, a paired comparison between both exams of the same eye was performed, to assess which made it easier to detect the lesions. Considering DR, the agreement was substantial for cotton wool spots and photocoagulation scars for both observers (O1: κ=0.75 and κ=0.67; O2: κ=0.71 and κ=0.64, respectively) and for hard exudates for O1 (κ=0.80). These lesions were detected more frequently on MFI. Regarding RVO, the agreement was considered substantial for venous sheathing by O1 (κ=0.64) and moderate for optociliary shunts by O2 (κ=0.60). Optociliary shunts were detected more frequently in CPF by both observers and venous sheathing on MFI by O1. For microaneurysms, retinal hemorrhages, retinal neovascularization, and proliferative membranes, in DR, and retinal hemorrhages, venous engorgement, and retinal neovascularization in RVO, the agreement was almost perfect (κ>0.82). In the paired analysis, both observers considered that, in DR, microaneurysms and retinal hemorrhages were easier to detect on CFP and that retinal neovascularization, cotton wool spots, and photocoagulation scars were easier to identify on MFI. Regarding RVO, optocilliary shunts were easier to identify on CFP and venous engorgement on MFI. The agreement of MFI and CFP was substantial to almost perfect for most lesions. MFI seems better to detect cotton wool spots and photocoagulations scars in DR and venous sheathing in RVO. Optocilliary shunts seem easier to detect on CFP.

Predicting the severity of white matter lesions among patients with cerebrovascular risk factors based on retinal images and clinical laboratory data: a deep learning study.

As one common feature of cerebral small vascular disease (cSVD), white matter lesions (WMLs) could lead to reduction in brain function. Using a convenient, cheap, and non-intrusive method to detect WMLs could substantially benefit to patient management in the community screening, especially in the settings of availability or contraindication of magnetic resonance imaging (MRI). Therefore, this study aimed to develop a useful model to incorporate clinical laboratory data and retinal images using deep learning models to predict the severity of WMLs. Two hundred fifty-nine patients with any kind of neurological diseases were enrolled in our study. Demographic data, retinal images, MRI, and laboratory data were collected for the patients. The patients were assigned to the absent/mild and moderate-severe WMLs groups according to Fazekas scoring system. Retinal images were acquired by fundus photography. A ResNet deep learning framework was used to analyze the retinal images. A clinical-laboratory signature was generated from laboratory data. Two prediction models, a combined model including demographic data, the clinical-laboratory signature, and the retinal images and a clinical model including only demographic data and the clinical-laboratory signature, were developed to predict the severity of WMLs. Approximately one-quarter of the patients (25.6%) had moderate-severe WMLs. The left and right retinal images predicted moderate-severe WMLs with area under the curves (AUCs) of 0.73 and 0.94. The clinical-laboratory signature predicted moderate-severe WMLs with an AUC of 0.73. The combined model showed good performance in predicting moderate-severe WMLs with an AUC of 0.95, while the clinical model predicted moderate-severe WMLs with an AUC of 0.78. Combined with retinal images from conventional fundus photography and clinical laboratory data are reliable and convenient approach to predict the severity of WMLs and are helpful for the management and follow-up of WMLs patients.

A practical clinical approach to traumatic choroidal rupture

Background: Traumatic choroidal rupture is a posterior segment manifestation of trauma and is more common in closed-globe injuries. It is defined as a tear of the choroid and Bruch’s membrane following blunt trauma. This narrative review summarizes the current literature and provides a practical clinical approach to the diagnosis and management of traumatic choroidal rupture and its complications. Methods: In this narrative review, we searched the PubMed/MEDLINE database using the search term choroidal rupture to provide a practical and updated approach to traumatic choroidal rupture, focusing on its definition, etiology, diagnosis, imaging, management of complications, and prognosis as mentioned in the literature over the last two decades. Results: Traumatic choroidal rupture occurs due to increased tensile stress on the eye wall and is three-fold more common in closed-globe injuries than in open injuries. Subretinal or sub-retinal pigment epithelial hemorrhage and macular edema are early signs. Macular involvement is associated with poor visual prognosis. Damage to Bruch’s membrane increases the risk of subretinal choroidal neovascular membrane (CNVM). Traumatic epiretinal membrane is another complication. Imaging modalities such as spectral-domain optical coherence tomography, indocyanine green angiography, conventional fundus fluorescein angiography, and optical coherence tomography angiography (OCT-A) can be used in diagnosis or monitoring for complications. OCT-A offers unique opportunities for the diagnosis, treatment, and follow-up of both the initial presentation and possible complications. Frequent follow-up has been suggested in the first year after trauma. Intravitreal anti-vascular endothelial growth factor (anti-VEGF) injection is effective and less invasive in managing CNVM. The visual outcome depends on the location of the rupture, baseline visual acuity, and presence of optic atrophy or macular holes. Risk factors for developing CNVM include rupture involving the macula, longer rupture length or rupture closer to the foveal center, and older age. Conclusions: Choroidal rupture is a posterior segment entity that usually occurs after trauma, compromises choroidal vessels and Bruch’s membrane, and can lead to CNVM. The use of novel noninvasive imaging modalities and efficient anti-VEGF therapy to manage this entity or its subsequent complications produces better visual outcomes. Early diagnosis and frequent follow-up of these patients allow treatment of possible complications, thereby improving the visual prognosis.