Retinal degeneration is a leading cause of vision impairment and blindness worldwide and medical care for advanced disease does not exist. Stem cell-derived retinal organoids (RtOgs) became an emerging tool for tissue replacement therapy. However, existing RtOg production methods are highly heterogeneous. Controlled and predictable methodology and tools are needed to standardize RtOg production and maintenance. In this study, we designed a shear stress-free micro-millifluidic bioreactor for nearly labor-free retinal organoid maintenance. We used a stereolithography (SLA) 3D printer to fabricate a mold from which Polydimethylsiloxane (PDMS) was cast. We optimized the chip design using in silico simulations and in vitro evaluation to optimize mass transfer efficiency and concentration uniformity in each culture chamber. We successfully cultured RtOgs at three different differentiation stages (day 41, 88, and 128) on an optimized bioreactor chip for more than 1 month. We used different quantitative and qualitative techniques to fully characterize the RtOgs produced by static dish culture and bioreactor culture methods. By analyzing the results from phase contrast microscopy, single-cell RNA sequencing (scRNA seq), quantitative polymerase chain reaction (qPCR), immunohistology, and electron microscopy, we found that bioreactor-cultured RtOgs developed cell types and morphology comparable to static cultured ones and exhibited similar retinal genes expression levels. We also evaluated the metabolic activity of RtOgs in both groups using fluorescence lifetime imaging (FLIM), and found that the outer surface region of bioreactor cultured RtOgs had a comparable free/bound NADH ratio and overall lower long lifetime species (LLS) ratio than static cultured RtOgs during imaging. To summarize, we validated an automated micro-millifluidic device with significantly reduced shear stress to produce RtOgs of comparable quality to those maintained in conventional static culture.

Purpose: Two-photon excitation fluorescence (2PEF) reveals information about tissue function. Concerns for phototoxicity demand lower light exposure during imaging. Reducing excitation light reduces the quality of the image by limiting fluorescence emission. We applied deep learning (DL) super-resolution techniques to images acquired from low light exposure to yield high-resolution images of retinal and skin tissues. Methods: We analyzed two methods: a method based on U-Net and a patch-based regression method using paired images of skin (550) and retina (1200), each with low- and high-resolution paired images. The retina dataset was acquired at low and high laser powers from retinal organoids, and the skin dataset was obtained from averaging 7 to 15 frames or 70 frames. Mean squared error (MSE) and the structural similarity index measure (SSIM) were outcome measures for DL algorithm performance. Results: For the skin dataset, the patches method achieved a lower MSE (3.768) compared with U-Net (4.032) and a high SSIM (0.824) compared with U-Net (0.783). For the retinal dataset, the patches method achieved an average MSE of 27,611 compared with 146,855 for the U-Net method and an average SSIM of 0.636 compared with 0.607 for the U-Net method. The patches method was slower (303 seconds) than the U-Net method (<1 second). Conclusions: DL can reduce excitation light exposure in 2PEF imaging while preserving image quality metrics. Translational Relevance: DL methods will aid in translating 2PEF imaging from benchtop systems to in vivo imaging of light-sensitive tissues such as the retina.

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: To evaluate the potential for artificial intelligence-based video analysis to determine surgical instrument characteristics when moving in the three-dimensional vitreous space. Methods: We 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. Results: The 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%. Conclusions: We 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 Relevance: Deep 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.

To report a case of cystoid macular edema, uveitis, and vitreomacular traction in a patient with a history of breast cancer and taking anastrozole. A 73-year-old female with a history of estrogen receptor-positive breast cancer and treatment with anastrozole presented with bilateral blurry vision, photophobia, and eye soreness. Optical coherence tomography (OCT) of both maculae revealed vitreomacular traction (VMT), an epiretinal membrane, cystoid macular edema (CME) in the right eye, and drusen without subretinal fluid bilaterally. Although later, macular OCT did show evidence of cystoid intraretinal spaces in the left eye as well. Fluorescein angiography showed bilateral petaloid leakage, bilateral slow disc leaking, as well as peripheral leakage in the right eye. Anastrozole was discontinued and, subsequent macular OCT showed release of VMT in the right eye, and eventual resolution of intraretinal cystoid spaces bilaterally. Stopping of anastrozole was associated in resolution of refractory CME in a patient on aromatase inhibitor therapy for breast cancer. It is therefore important to consider anastrozole and other aromatase inhibitor drugs as possible factors predisposing patients to the development of CME.