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Retinal degeneration (RD) is a significant cause of incurable blindness worldwide. Photoreceptors and retinal pigmented epithelium are irreversibly damaged in advanced RD. Functional replacement of photoreceptors and/or retinal pigmented epithelium cells is a promising approach to restoring vision. This paper reviews the current status and explores future prospects of the transplantation therapy provided by pluripotent stem cell-derived retinal organoids (ROs). This review summarizes the status of rodent RD disease models and discusses RO culture and analytical tools to evaluate RO quality and function. Finally, we review and discuss the studies in which RO-derived cells or sheets were transplanted. In conclusion, methods to derive ROs from pluripotent stem cells have significantly improved and become more efficient in recent years. Meanwhile, more novel technologies are applied to characterize and validate RO quality. However, opportunity remains to optimize tissue differentiation protocols and achieve better RO reproducibility. In order to screen high-quality ROs for downstream applications, approaches such as noninvasive and label-free imaging and electrophysiological functional testing are promising and worth further investigation. Lastly, transplanted RO-derived tissues have allowed improvements in visual function in several RD models, showing promises for clinical applications in the future.

PurposeTo evaluate the potential for artificial intelligence-based video analysis to determine surgical instrument characteristics when moving in the three-dimensional vitreous space.MethodsWe designed and manufactured a model eye in which we recorded choreographed videos of many surgical instruments moving throughout the eye. We labeled each frame of the videos to describe the surgical tool characteristics: tool type, location, depth, and insertional laterality. We trained two different deep learning models to predict each of the tool characteristics and evaluated model performances on a subset of images.ResultsThe accuracy of the classification model on the training set is 84% for the x-y region, 97% for depth, 100% for instrument type, and 100% for laterality of insertion. The accuracy of the classification model on the validation dataset is 83% for the x-y region, 96% for depth, 100% for instrument type, and 100% for laterality of insertion. The close-up detection model performs at 67 frames per second, with precision for most instruments higher than 75%, achieving a mean average precision of 79.3%.ConclusionsWe demonstrated that trained models can track surgical instrument movement in three-dimensional space and determine instrument depth, tip location, instrument insertional laterality, and instrument type. Model performance is nearly instantaneous and justifies further investigation into application to real-world surgical videos.Translational relevanceDeep learning offers the potential for software-based safety feedback mechanisms during surgery or the ability to extract metrics of surgical technique that can direct research to optimize surgical outcomes.

PurposeThis report aims to characterize ocular changes in a case of ocular siderosis with iron toxicity using multimodal imaging and electroretinography.MethodsA 34-year-old woman presented with ocular siderosis of the left eye following penetrating injury with an iron-containing foreign body. The patient's uncorrected visual acuities were 20/60 and 20/150 in the right and left eye, respectively, with abnormal pupillary function and presence of a cataract in the left eye. She underwent successful intraocular foreign body removal and cataract surgery with no postoperative complications. Cone contrast threshold (CCT), full-field electroretinogram, spectral-domain optical coherence tomography (OCT), and OCT angiography (OCTA) were used to characterize ocular alterations preoperatively and postoperatively.ResultsCCT color vision testing showed abnormal color vision, and OCTA revealed increased vascular flow density associated with the foreign body.ConclusionsCCT color vision testing, OCTA, OCT, and full-field electroretinogram can characterize retinal changes in cases of ocular siderosis.

Two-photon excitation fluorescence (2PEF) allows imaging of tissue up to about one millimeter in thickness. Typically, reducing fluorescence excitation exposure reduces the quality of the image. However, using deep learning super resolution techniques, these low-resolution images can be converted to high-resolution images. This work explores improving human tissue imaging by applying deep learning to maximize image quality while reducing fluorescence excitation exposure. We analyze two methods: a method based on U-Net, and a patch-based regression method. Both methods are evaluated on a skin dataset and an eye dataset. The eye dataset includes 1200 paired high power and low power images of retinal organoids. The skin dataset contains multiple frames of each sample of human skin. High-resolution images were formed by averaging 70 frames for each sample and low-resolution images were formed by averaging the first 7 and 15 frames for each sample. The skin dataset includes 550 images for each of the resolution levels. We track two measures of performance for the two methods: mean squared error (MSE) and structural similarity index measure (SSIM). For the eye dataset, the patches method achieves an average MSE of 27,611 compared to 146,855 for the U-Net method, and an average SSIM of 0.636 compared to 0.607 for the U-Net method. For the skin dataset, the patches method achieves an average MSE of 3.768 compared to 4.032 for the U-Net method, and an average SSIM of 0.824 compared to 0.783 for the U-Net method. Despite better performance on image quality, the patches method is worse than the U-Net method when comparing the speed of prediction, taking 303 seconds to predict one image compared to less than one second for the U-Net method.

Background: Visual impairment, specifically anterior segment pathology, presents a significant burden of disease in the world. Introduction: Inexpensive tools are necessary to improve eye health of residents in developing countries where care is difficult to access. Our study aimed at determining whether a $5 macro lens attached to a smartphone camera is an effective anterior segment imaging method for screening diseases. Materials and Methods: Fifty four (n = 54) patients had anterior segment imaging performed by using an Easy Macro lens and an iPhone. Imaging was performed at the Floating Doctors' mobile clinic sites in Panama. Images were sent back and graded by two board-certified ophthalmologists using a modified version of the FOTO-ED scale. Statistical analysis was performed by using a Wilcoxon signed-rank test to compare grades between the two imaging modalities. Results: There was no significant difference in overall clinical utility of images obtained by the iPhone versus Easy Macro lens. The iPhone was significantly superior in imaging of the lens and conjunctiva, whereas the Easy Macro lens was superior in regards to the anterior chamber, iris, and lens. Discussion: The imaging modality that best captures pathology is dependent on what part of the anterior segment is being examined. An imaging protocol with a pair of images, one from a smartphone and one from a macro lens, would have significant clinical utility. Conclusion: Our study demonstrates how minimally trained users can deliver effective eye screening via a telemedicine-based approach in a resource-deprived setting. Future directions would be to develop a telemedicine protocol and determine whether it improves clinically measurable outcomes in patients.

PURPOSE:: Quantitative end points for uveitis are needed. Here we quantify the rate of ellipsoid zone (EZ) recovery on optical coherence tomography (OCT) and correlate it with visual acuity (VA) improvement in patients with acute posterior multifocal placoid pigmented epitheliopathy (APMPPE). We use automated and manually graded EZ area analysis to assess EZ recovery in APMPPE. METHODS:: We performed a retrospective review of 9 APMPPE cases (18 eyes) that had characteristic clinical examination and fluorescein angiography findings, outer retinal disruption on spectral-domain OCT, and treatment with systemic steroids after an unambiguous laboratory workup. The EZ was delineated using custom software to perform automated analysis and manual grading by 2 independent physicians. Quantitation of EZ changes was performed in ImageJ (National Institutes of Health). EZ maps were compared with equivalent findings from EZ en face OCT segmentation. RESULTS:: The 9 cases in our study were followed for an average of 198 days. Symptomatic improvement occurred in all eyes. VA recovery occurred in 83% of eyes and depended on presenting foveal involvement. Positive slopes of EZ area over time demonstrated recovery. EZ recovery profiles determined by manual and automated software demonstrated high Pearson correlation coefficients (0.78-0.94). Slab en face EZ analysis demonstrated moderate agreement. CONCLUSIONS:: EZ recovery correlates with symptomatic and VA recovery. Automated EZ analysis shows strong agreement with manually graded EZ analysis in APMPPE. EZ recovery in patients with APMPPE provides a biomarker for recovery and may be applied to other diseases affecting the outer retina.

Objective: To develop software utilizing optical character recognition toward the automatic extraction of data from bar charts for meta-analysis. Methods: We utilized a multistep data extraction approach that included figure extraction, text detection, and image disassembly. PubMed Central papers that were processed in this manner included clinical trials regarding macular degeneration, a disease causing blindness with a heavy disease burden and many clinical trials. Bar chart characteristics were extracted in both an automated and manual fashion. These two approaches were then compared for accuracy. These characteristics were then compared using a Bland-Altman analysis. Results: Based on Bland-Altman analysis, 91.8% of data points were within the limits of agreement. By comparing our automated data extraction with manual data extraction, automated data extraction yielded the following accuracies: X-axis labels 79.5%, Y-tick values 88.6%, Y-axis label 88.6%, Bar value

PurposeSilicone oil (SO) is often used as an intraocular tamponade in repairs of retinal detachments. It may be associated with complications such as cataract, glaucoma, keratopathy, subretinal migration of oil, fibrous epiretinal and sub retinal proliferations, and oil emulsification. The purpose of this report is to describe a rare phenomenon of intraocular silicone oil migration into the cerebral ventricles, which may later be mistaken for intraventricular hemorrhages on neuroimaging.MethodsCase report with literature review.ResultsA patient with a history of retinal detachment repair with intraocular SO presented with headaches. Neuroimaging revealed SO migration to the cerebral ventricles. The patient was treated conservatively with symptom management and headaches resolved.ConclusionsWe present a case of intraocular SO migration to the cerebral ventricles and review the current literature. We also propose two mechanisms for this phenomenon.

TOPIC: To assess the role and utility of intraoperative optical coherence tomography (iOCT) guidance during pars plana vitrectomy with chorioretinal biopsy for patients with suspicious chorioretinal lesions. CLINICAL RELEVANCE: Chorioretinal lesions suspicious for malignancy sometimes require surgical biopsy for diagnosis and treatment guidance. Biopsy site selection is currently guided by presurgical imaging, and clinical assessment. We sought to determine whether iOCT provides useful information for biopsy site selection to ensure a diagnostic specimen and assessment of wound repair after biopsy. MATERIALS AND METHODS: DISCOVER is a prospective study examining microscope-integrated iOCT systems in ophthalmic surgery. In this report, we examine all eyes in the DISCOVER study that underwent chorioretinal biopsy. Clinical characteristics, surgical technique, and imaging findings were reviewed. In addition, surgeon feedback data on iOCT utility and value to the procedure was also evaluated. RESULTS: Six eyes were identified within the DISCOVER study that underwent chorioretinal biopsy. iOCT was used to assess retinal and choroidal lesions prior to chorioretinal incision. Following biopsy, iOCT was utilized to assess retinal attachment and wound margin integrity. Additionally, iOCT was performed in real-time during biopsy to ensure proper biopsy depth and position. In 2 cases, preoperative OCT evaluation of retinal lesions was not possible. Utilizing iOCT, comprehensive evaluation of anatomic features was successfully performed prior to biopsy. This facilitated identification of biopsy sites most likely to provide a diagnostic sample based on lesion morphology on iOCT. After completion of chorioretinal biopsy, iOCT verified completeness of biopsy site and the retinal-RPE relationship (e.g., complete apposition, partial detachment). CONCLUSION: Microscope-integrated iOCT provides real-time guidance of biopsy site selection and verification of biopsy site stability following specimen collection.