Fundus autofluorescence (FAF) is a prompt and non-invasive imaging modality helpful in detecting pathological abnormalities within the retina and the choroid. This narrative review and case series provides an overview on the current application of FAF in posterior and panuveitis. The literature was reviewed for articles on lesion characteristics on FAF of specific posterior and panuveitis entities as well as benefits and limitations of FAF for diagnosing and monitoring disease. FAF characteristics are described for non-infectious and infectious uveitis forms as well as masquerade syndromes. Dependent on the uveitis entity, FAF is of diagnostic value in detecting disease and following the clinical course. Currently available FAF modalities which differ in excitation wavelengths can provide different pathological insights depending on disease entity and activity. Further studies on the comparison of FAF modalities and their individual value for uveitis diagnosis and monitoring are warranted.

This study aims to investigate the relationship between pachychoroid spectrum disorders and retinitis pigmentosa (RP) or rod-cone dystrophy through a comprehensive literature review. The purpose is to explore the association between these disorders, understand their underlying mechanisms, and summarize the existing hypotheses and opinions. A thorough review of the literature was conducted using PubMed, focusing on articles related to central serous chorioretinopathy (CSC), RP, pachychoroid pigment epitheliopathy, pachychoroid neovasculopathy, polypoidal choroidal vasculopathy, focal choroidal excavation, peripapillary pachychoroid neovasculopathy, and peripheral exudative hemorrhagic chorioretinopathy. Relevant studies were selected for a detailed narrative review and analysis. Several studies have reported the coexistence of CSC and RP, indicating a potential association between the two conditions. The dysfunction of the retinal pigment epithelium is proposed as a common factor. Choroidal thinning is observed in RP, but conflicting results exist regarding choroidal thickness (CT). While some studies support choroidal thinning in RP, others suggest preserved or increased thickness. Additionally, cases of pachychoroid neovasculopathy and polypoidal choroidal vasculopathy in RP have been reported, suggesting an overlap between these conditions. The literature suggests conflicting reports on CT changes in RP. Future research should focus on large-scale studies using comprehensive imaging techniques, genetic analysis, and long-term follow-up to uncover the underlying mechanisms and determine the prevalence of pachychoroid spectrum disorders in RP patients.

Diabetic retinopathy (DR) is characterized by retinal vasculopathy and is a leading cause of visual impairment. Optical coherence tomography angiography (OCTA) is an innovative imaging technology that can detect various pathologies and quantifiable changes in retinal microvasculature. We briefly describe its functional principles and advantages over fluorescein angiography and perform a comprehensive review on its clinical applications in the screening or management of people with prediabetes, diabetes without clinical retinopathy (NDR), nonproliferative DR (NPDR), proliferative DR (PDR), and diabetic macular edema (DME). OCTA reveals early microvascular alterations in prediabetic and NDR eyes, which may coexist with sub-clinical neuroretinal dysfunction. Its applications in NPDR include measuring ischemia, detecting retinal neovascularization, and timing of early treatment through predicting the risk of retinopathy worsening or development of DME. In PDR, OCTA helps characterize the flow within neovascular complexes and evaluate their progression or regression in response to treatment. In eyes with DME, OCTA perfusion parameters may be of predictive value regarding the visual and anatomical gains associated with treatment. We further discussed the limitations of OCTA and the benefits of its incorporation into an updated DR severity scale.

Diabetic retinopathy (DR) is the leading global cause of vision loss, accounting for 4.8% of global blindness cases as estimated by the World Health Organization (WHO). Fundus photography is crucial in ophthalmology as a diagnostic tool for capturing retinal images. However, resource and infrastructure constraints limit access to traditional tabletop fundus cameras in developing countries. Additionally, these conventional cameras are expensive, bulky, and not easily transportable. In contrast, the newer generation of handheld and smartphone-based fundus cameras offers portability, user-friendliness, and affordability. Despite their potential, there is a lack of comprehensive review studies examining the clinical utilities of these handheld (e.g. Zeiss Visuscout 100, Volk Pictor Plus, Volk Pictor Prestige, Remidio NMFOP, FC161) and smartphone-based (e.g. D-EYE, iExaminer, Peek Retina, Volk iNview, Volk Vistaview, oDocs visoScope, oDocs Nun, oDocs Nun IR) fundus cameras. This review study aims to evaluate the feasibility and practicality of these available handheld and smartphone-based cameras in medical settings, emphasizing their advantages over traditional tabletop fundus cameras. By highlighting various clinical settings and use scenarios, this review aims to fill this gap by evaluating the efficiency, feasibility, cost-effectiveness, and remote capabilities of handheld and smartphone fundus cameras, ultimately enhancing the accessibility of ophthalmic services.

BACKGROUND: Analyzing fundus images with deep learning techniques is promising for screening systematic diseases. However, the quality of the rapidly increasing number of studies was variable and lacked systematic evaluation.
OBJECTIVE: To systematically review all the articles that aimed to predict systemic parameters and conditions using fundus image and deep learning, assessing their performance, and providing suggestions that would enable translation into clinical practice.
METHODS: Two major electronic databases (MEDLINE and EMBASE) were searched until August 22, 2023, with keywords \'deep learning\' and \'fundus\'. Studies using deep learning and fundus images to predict systematic parameters were included, and assessed in four aspects: study characteristics, transparent reporting, risk of bias, and clinical availability. Transparent reporting was assessed by the TRIPOD statement, while the risk of bias was assessed by PROBAST.
RESULTS: 4969 articles were identified through systematic research. Thirty-one articles were included in the review. A variety of vascular and non-vascular diseases can be predicted by fundus images, including diabetes and related diseases (19%), sex (22%) and age (19%). Most of the studies focused on developed countries. The models\' reporting was insufficient in determining sample size and missing data treatment according to the TRIPOD. Full access to datasets and code was also under-reported. 1/31(3.2%) study was classified as having a low risk of bias overall, whereas 30/31(96.8%) were classified as having a high risk of bias according to the PROBAST. 5/31(16.1%) of studies used prospective external validation cohorts. Only two (6.4%) described the study\'s calibration. The number of publications by year increased significantly from 2018 to 2023. However, only two models (6.5%) were applied to the device, and no model has been applied in clinical.
CONCLUSIONS: Deep learning fundus images have shown great potential in predicting systematic conditions in clinical situations. Further work needs to be done to improve the methodology and clinical application.
摘要: 背景: 深度学习技术分析眼底图像有助于筛查全身性疾病。然而, 数量迅速增加的研究, 其质量存在差异, 并且缺乏系统性评估。目的: 对所有旨在使用眼底图像和深度学习预测系统参数和条件的文献进行系统回顾, 评估其性能, 并提供促进临床实践转化的建议。方法: 截至2023年8月22日, 使用关键词“深度学习”和“眼底”检索了两个主要的电子数据库 (MEDLINE和EMBASE) 。包括使用深度学习和眼底图像预测系统参数的研究, 并从四个方面进行评估: 研究特征、透明报告、偏倚风险和临床可用性。透明报告由TRIPOD声明评估, 而偏倚风险由PROBAST评估。结果: 系统性搜索共4969篇文章, 其中31篇被纳入综述。眼底图像可预测各种血管性和非血管性疾病, 包括糖尿病及相关疾病 (19%) 、性别 (22%) 和年龄 (19%) 。大多数研究集中在发达国家。根据TRIPOD, 在确定样本大小和缺失数据处理方面的报告不足。对于数据库和代码完整访问的报道也存在不足。根据PROBAST, 1/31 (3.2%) 被分类为总体偏倚风险低, 而30/31 (96.8%) 被分类为总体偏倚风险高。而5/31 (16.1%) 研究使用了前瞻性外部验证队列。只有两项 (6.4%) 描述了研究的校准情况。从2018年到2023年, 每年的发表文献数量显著增加。然而, 只有两个模型 (6.5%) 应用于设备, 没有模型应用于临床。结论: 深度学习眼底图像在预测临床情况方面展现出巨大潜力。需要进一步努力改进方法学和临床应用。.

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The authors report a rare case of pediatric central retinal artery occlusion (CRAO) and spontaneous late recovery, with associated literature review. A retrospective review of patient medical records and literature search of four relevant databases was conducted. A 7-year-old girl with sudden, painless loss of vision in her right eye was referred to the authors\' center where she was found to have a CRAO with hand motion visual acuity. One month later, the patient developed multiple retinal hemorrhages throughout the fundus. Despite undergoing extensive investigations, a cause for her condition was not determined. Seventeen months after the original event, the patient regained a visual acuity of 20/40. A CRAO in the pediatric population is relatively rare and requires extensive investigation into the potential cause. Compared to adult CRAO, presentation in the pediatric age group should warrant a high index of suspicion and extensive work-up to treat any underlying systemic conditions and possibly prevent involvement of the other eye. [J Pediatr Ophthalmol Strabismus. 2023;60(6):e70-e74.].

Microperimetry is an emerging technology that provides concurrent analysis of retinal structure and function by combining retinal sensitivity and fixation analysis with fundus imaging. We summarize the substantial evidence validating the evolving role of microperimetry as an adjunctive assessment of visual function in the perioperative setting. We show that microperimetry provides useful complementary information to other established imaging and functional modalities in the perioperative setting for a wide range of vitreoretinal surgical procedures, as well as in cataract and refractive surgeries. This includes preoperative uses such as prognostication of visual and anatomical outcomes, timing of surgical intervention, and assessment of patient suitability for surgery-as well as postoperative uses including quantification of visual recovery, investigation of unexplained postoperative vision loss, and informing expected long term functional outcomes.

OBJECTIVE: To report a rare case of unilateral central retinal artery occlusion (CRAO) following spinal surgery.
METHODS: Observational case report.
RESULTS: A 15-year-old female patient underwent scoliosis surgery under general anesthesia in a prone position, her head being supported by a horseshoe headrest for approximately four hours, with stable vitals and without significant blood loss during surgery. Upon waking up from general anesthesia, the patient immediately reported severe visual loss in her right eye (RE), associated to marked periocular ecchymosis and chemosis. Visual acuity was limited to light perception. Fundus examination showed normal optic disc appearance with diffuse retinal pallor and a macular cherry red spot. Optical coherence tomography (OCT) showed increased reflectivity in the inner retina, consistent with ischemic maculopathy in the RE. Brain and neck magnetic resonance imaging angiograms were unremarkable. Further investigations ruled out collagen vascular disease, Behcet disease, syphilis, sickle cell disease and hypercoagulable states.
CONCLUSIONS: Central retinal artery occlusion is rarely observed following spinal surgery. The cause was presumed to be compression of the orbit by a horseshoe headrest in a prone position due to an accidental shift in position during surgery. This catastrophic complication, albeit rare, is usually irreversible and thus must be prevented. Proper positioning and vigilance by both the surgeon and the anesthesiologist during surgery are fundamental to ensure that the orbits are not under pressure.

OBJECTIVE: Deep learning has been used to detect chronic kidney disease (CKD) from retinal fundus photographs. We aim to evaluate the performance of deep learning for CKD detection.
METHODS: The original studies in CKD patients detected by deep learning from retinal fundus photographs were eligible for inclusion. PubMed, Embase, the Cochrane Library, and Web of Science were searched up to October 31, 2022. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the risk of bias.
RESULTS: Four studies enrolled 114,860 subjects were included. The pooled sensitivity and specificity were 87.8% (95% confidence interval (CI): 61.6% to 98.3%), and 62.4% (95% CI: 44.9% to 78.7%). The area under the curve (AUC) was 0.864 (95%CI: 0.769, 0.986).
CONCLUSIONS: Deep learning based on retinal fundus photographs has the ability to detect CKD, but it currently has a lot of room for improvement. It is still a long way from clinical application.