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High-quality imaging of the retina is crucial to the diagnosis and monitoring of disease, as well as for evaluating the success of therapeutics in human patients and in preclinical animal models. Here, we describe the basic principles and methods for in vivo retinal imaging in rodents, including fundus imaging, fluorescein angiography, optical coherence tomography, fundus autofluorescence, and infrared imaging. After providing a concise overview of each method and detailing the retinal diseases and conditions that can be visualized through them, we will proceed to discuss the advantages and disadvantages of each approach. These protocols will facilitate the acquisition of optimal images for subsequent quantification and analysis. Additionally, a brief explanation will be given regarding the potential results and the clinical significance of the detected abnormalities.

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There is a growing number of publicly available ophthalmic imaging datasets and open-source code for Machine Learning algorithms. This allows ophthalmic researchers and practitioners to independently perform various deep-learning tasks. With the advancement in artificial intelligence (AI) and in the field of imaging, the choice of the most appropriate AI architecture for different tasks will vary greatly. The best-performing AI-dataset combination will depend on the specific problem that needs to be solved and the type of data available. The article discusses different machine learning models and deep learning architectures currently used for various ophthalmic imaging modalities and for different machine learning tasks. It also proposes the most appropriate models based on accuracy and other important factors such as training time, the ability to deploy the model on clinical devices/smartphones, heatmaps that enhance the self-explanatory nature of classification decisions, and the ability to train/adapt on small image datasets to determine if further data collection is worthwhile. The article extensively reviews the existing state-of-the-art AI methods focused on useful machine-learning applications for ophthalmology. It estimates their performance and viability through training and evaluating architectures with different public and private image datasets of different modalities, such as full-color retinal images, OCT images, and 3D OCT scans. The article is expected to benefit the readers by enriching their knowledge of artificial intelligence applied to ophthalmology.

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Purpose: To report the application of an infrared fundus imaging and navigated laser system to photocoagulate a nematode in diffuse unilateral subacute neuroretinitis (DUSN). Observations: A 14-year-old boy with DUSN was treated with systemic albendazole and corticosteroids. Laser photocoagulation of the visible nematode was performed using a navigated laser in live infrared fundus view (Navilas 577s, OD-OS GmbH, Berlin, Germany). While the localization of the nematode was difficult in regular fundoscopy due to the light-shy helminth, it could be well localized and targeted with the infrared live video mode and navigated laser system. No inflammatory flare-up was observed after the nematode was killed. Conclusions and Importance: Laser photocoagulation and systemic antihelminthic therapy are an established treatment for DUSN. Infrared imaging and navigated laser systems seem useful in targeting and killing mobile nematodes.

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Age-related macular degeneration (AMD) is a leading cause of blindness. It is associated with peripheral drusen which has not been categorized. We investigated peripheral drusen to validate an image grading system and to understand possible associations between peripheral drusen and AMD. We collated clinical data, ultra-widefield (UWF) pseudocolor fundus images and Spectral-Domain Optical Coherence Tomography (SD-OCT) scans from consecutive retinal patients. SD-OCT scans were used to determine AMD stage. A masked retinal specialist recorded the types of peripheral drusen observed in UWF images. Eyes whose UWF images did not pass quality screening and those without AMD and peripheral drusen were excluded from the study. Statistical tests were utilized to determine the validity of our grading system and associations of peripheral drusen with AMD. A total of 481 eyes (283 subjects) were included in the study (mean age 73.1 ± 1.2years, 64.3% female). Interobserver and test-retest statistical analyses to evaluate the UWF image grading system resulted in Cohen's Kappa 0.649 (p < 0.001) and 0.922 (p < 0.001) respectively. A total of 284 (59.0%), 28 (5.8%), 15 (3.1%), 22 (4.6%), 4 (0.8%), 39 (8.1%), and 32 (6.7%) eyes had hard, soft, reticular, cuticular, atrophic, mixed drusen, and mixed drusen and atrophy respectively in at least one peripheral retinal quadrant. Hard peripheral drusen was significantly associated with the presence of AMD (p = 0.010). Peripheral drusen types were variably seen in retinal patients with and without AMD. We validated a peripheral drusen grading system and provided an image library to assist in the identification of peripheral drusen. Our study found an association between peripheral hard drusen and an AMD diagnosis but did not find a link between peripheral drusen and severity of AMD.

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Background: This study aimed to evaluate the potential of human-machine interaction (HMI) in a deep learning software for discerning the malignancy of choroidal melanocytic lesions based on fundus photographs. Methods: The study enrolled individuals diagnosed with a choroidal melanocytic lesion at a tertiary clinic between 2011 and 2023, resulting in a cohort of 762 eligible cases. A deep learning-based assistant integrated into the software underwent training using a dataset comprising 762 color fundus photographs (CFPs) of choroidal lesions captured by various fundus cameras. The dataset was categorized into benign nevi, untreated choroidal melanomas, and irradiated choroidal melanomas. The reference standard for evaluation was established by retinal specialists using multimodal imaging. Trinary and binary models were trained, and their classification performance was evaluated on a test set consisting of 100 independent images. The discriminative performance of deep learning models was evaluated based on accuracy, recall, and specificity. Results: The final accuracy rates on the independent test set for multi-class and binary (benign vs. malignant) classification were 84.8% and 90.9%, respectively. Recall and specificity ranged from 0.85 to 0.90 and 0.91 to 0.92, respectively. The mean area under the curve (AUC) values were 0.96 and 0.99, respectively. Optimal discriminative performance was observed in binary classification with the incorporation of a single imaging modality, achieving an accuracy of 95.8%. Conclusions: The deep learning models demonstrated commendable performance in distinguishing the malignancy of choroidal lesions. The software exhibits promise for resource-efficient and cost-effective pre-stratification.

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Viral infection frequently affects the cardiovascular system, and vascular disturbances in patients can lead to health complications. One essential component of the cardiovascular system that is vulnerable to the inflammatory effects of viral infections is the microcirculatory system. As a suitable and practical non-invasive method to assess the structure and function of the retinal microcirculation, a proxy for the microcirculatory system, retinal fundus imaging can be used. We examined the impact of viral infections on retinal vessel diameters and performed a systematic analysis of the literature. Our search was carried out on PubMed using predefined search queries. After a methodological filtering process, we were able to reduce the corpus of 363 publications to 16 studies that met the search parameters. We used a narrative review style to summarise the observations. Six studies covered COVID-19, seven described HIV, and three were included in the subgroup called others, covering viruses, such as Dengue Fever and Crimean-Congo Haemorrhagic Fever. Analysis of the literature showed that viral infections are associated with alterations in the retinal vessels' vasoactivity. COVID-19 and other infections cause inflammation-associated the vasodilatation of microvasculature as a short-term effect of the infection. Long COVID-19 as well as HIV are the cause of chronic inflammation impacting microvascular morphology via retinal vessel diameter narrowing. The review emphasises the importance of the understudied area of viral infections' effects on retinal microcirculation. Continuous research in this area is needed to further verify retinal fundus imaging as an innovative tool for the optimal diagnosis of microvascular changes. As changes in the microvasculature precede changes in bigger arteries, the early detection of microvascular changes can go a long way in reducing the morbidity and mortality associated with cardiovascular diseases.

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The advent of smartphone fundus imaging technology has marked a significant evolution in the field of ophthalmology, offering a novel approach to the diagnosis and management of retinopathy. This review provides an overview of smartphone fundus imaging, including clinical applications, advantages, limitations, clinical applications, and future directions. The traditional fundus imaging techniques are limited by their cost, portability, and accessibility, particularly in resource-limited settings. Smartphone fundus imaging emerges as a cost-effective, portable, and accessible alternative. This technology facilitates the early detection and monitoring of various retinal pathologies, including diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders, thereby democratizing access to essential diagnostic services. Despite its advantages, smartphone fundus imaging faces challenges in image quality, standardization, regulatory considerations, and medicolegal issues. By addressing these limitations, this review highlights the areas for future research and development to fully harness the potential of smartphone fundus imaging in enhancing patient care and visual outcomes. The integration of this technology into telemedicine is also discussed, underscoring its role in facilitating remote patient care and collaborative care among physicians. Through this review, we aim to contribute to the understanding and advancement of smartphone fundus imaging as a valuable tool in ophthalmic practice, paving the way for its broader adoption and integration into medical diagnostics.

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Fundus photography (FP) is a crucial technique for diagnosing the progression of ocular and systemic diseases in clinical studies, with wide applications in early clinical screening and diagnosis. However, due to the nonuniform illumination and imbalanced intensity caused by various reasons, the quality of fundus images is often severely weakened, brings challenges for automated screening, analysis, and diagnosis of diseases. To resolve this problem, we developed strongly constrained generative adversarial networks (SCGAN). The results demonstrate that the quality of various datasets were more significantly enhanced based on SCGAN, simultaneously more effectively retaining tissue and vascular information under various experimental conditions. Furthermore, the clinical effectiveness and robustness of this model were validated by showing its improved ability in vascular segmentation as well as disease diagnosis. Our study provides a new comprehensive approach for FP and also possesses the potential capacity to advance artificial intelligence-assisted ophthalmic examination.

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Retinal diseases stand as a primary cause of childhood blindness. Analyzing the progression of these diseases requires close attention to lesion morphology and spatial information. Standard image registration methods fail to accurately reconstruct pediatric fundus images containing significant distortion and blurring. To address this challenge, we proposed a robust deep learning-based image registration method (RDLR). The method consisted of two modules: registration module (RM) and panoramic view module (PVM). RM effectively integrated global and local feature information and learned prior information related to the orientation of images. PVM was capable of reconstructing spatial information in panoramic images. Furthermore, as the registration model was trained on over 280,000 pediatric fundus images, we introduced a registration annotation automatic generation process coupled with a quality control module to ensure the reliability of training data. We compared the performance of RDLR to the other methods, including conventional registration pipeline (CRP), voxel morph (WM), generalizable image matcher (GIM), and self-supervised techniques (SS). RDLR achieved significantly higher registration accuracy (average Dice score of 0.948) than the other methods (ranging from 0.491 to 0.802). The resulting panoramic retinal maps reconstructed by RDLR also demonstrated substantially higher fidelity (average Dice score of 0.960) compared to the other methods (ranging from 0.720 to 0.783). Overall, the proposed method addressed key challenges in pediatric retinal imaging, providing an effective solution to enhance disease diagnosis. Our source code is available at https://github.com/wuwusky/RobustDeepLeraningRegistration .

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Purpose: To utilize ultrawidefield (UWF) imaging to evaluate retinal and choroidal vasculature and structure in individuals with mild cognitive impairment (MCI) compared with that of controls with normal cognition. Design: Prospective cross sectional study. Participants: One hundred thirty-one eyes of 82 MCI patients and 230 eyes of 133 cognitively normal participants from the Eye Multimodal Imaging in Neurodegenerative Disease Study. Methods: A scanning laser ophthalmoscope (California, Optos Inc) was used to obtain UWF fundus color images. Images were analyzed with the Vasculature Assessment Platform for Images of the Retina UWF (VAMPIRE-UWF 2.0, Universities of Edinburgh and Dundee) software. Main outcome measures: Imaging parameters included vessel width gradient, vessel width intercept, large vessel choroidal vascular density, vessel tortuosity, and vessel fractal dimension. Results: Both retinal artery and vein width gradients were less negative in MCI patients compared with controls, demonstrating decreased rates of vessel thinning at the periphery (P < 0.001; P = 0.027). Retinal artery and vein width intercepts, a metric that extrapolates the width of the vessel at the center of the optic disc, were smaller in MCI patients compared with that of controls (P < 0.001; P = 0.017). The large vessel choroidal vascular density, which quantifies the vascular area versus the total choroidal area, was greater in MCI patients compared with controls (P = 0.025). Conclusions: When compared with controls with normal cognition, MCI patients had thinner retinal vasculature manifested in both the retinal arteries and the veins. In MCI, these thinner arteries and veins attenuated at a lower rate when traveling toward the periphery. MCI patients also had increased choroidal vascular density. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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Significance: Retinopathy of prematurity (ROP) poses a significant global threat to childhood vision, necessitating effective screening strategies. This study addresses the impact of color channels in fundus imaging on ROP diagnosis, emphasizing the efficacy and safety of utilizing longer wavelengths, such as red or green for enhanced depth information and improved diagnostic capabilities. Aim: This study aims to assess the spectral effectiveness in color fundus photography for the deep learning classification of ROP. Approach: A convolutional neural network end-to-end classifier was utilized for deep learning classification of normal, stage 1, stage 2, and stage 3 ROP fundus images. The classification performances with individual-color-channel inputs, i.e., red, green, and blue, and multi-color-channel fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared. Results: For individual-color-channel inputs, similar performance was observed for green channel (88.00% accuracy, 76.00% sensitivity, and 92.00% specificity) and red channel (87.25% accuracy, 74.50% sensitivity, and 91.50% specificity), which is substantially outperforming the blue channel (78.25% accuracy, 56.50% sensitivity, and 85.50% specificity). For multi-color-channel fusion options, the early-fusion and intermediate-fusion architecture showed almost the same performance when compared to the green/red channel input, and they outperformed the late-fusion architecture. Conclusions: This study reveals that the classification of ROP stages can be effectively achieved using either the green or red image alone. This finding enables the exclusion of blue images, acknowledged for their increased susceptibility to light toxicity.

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Circulating inflammatory cells in eyes have emerged as early indicators of numerous major diseases, yet the monitoring of these cells remains an underdeveloped field. In vivo flow cytometry (IVFC), a noninvasive technique, offers the promise of real-time, dynamic quantification of circulating cells. However, IVFC has not seen extensive applications in the detection of circulating cells in eyes, possibly due to the eye's unique physiological structure and fundus imaging limitations. This study reviews the current research progress in retinal flow cytometry and other fundus examination techniques, such as adaptive optics, ultra-widefield retinal imaging, multispectral imaging, and optical coherence tomography, to propose novel ideas for circulating cell monitoring.

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BACKGROUND: Artificial intelligence (AI) has the potential to increase the affordability and accessibility of eye disease screening, especially with the recent approval of AI-based diabetic retinopathy (DR) screening programs in several countries. METHODS: This study investigated the performance, feasibility, and user experience of a seamless hardware and software solution for screening chronic eye diseases in a real-world clinical environment in Germany. The solution integrated AI grading for DR, age-related macular degeneration (AMD), and glaucoma, along with specialist auditing and patient referral decision. The study comprised several components: (1) evaluating the entire system solution from recruitment to eye image capture and AI grading for DR, AMD, and glaucoma; (2) comparing specialist's grading results with AI grading results; (3) gathering user feedback on the solution. RESULTS: A total of 231 patients were recruited, and their consent forms were obtained. The sensitivity, specificity, and area under the curve for DR grading were 100.00%, 80.10%, and 90.00%, respectively. For AMD grading, the values were 90.91%, 78.79%, and 85.00%, and for glaucoma grading, the values were 93.26%, 76.76%, and 85.00%. The analysis of all false positive cases across the three diseases and their comparison with the final referral decisions revealed that only 17 patients were falsely referred among the 231 patients. The efficacy analysis of the system demonstrated the effectiveness of the AI grading process in the study's testing environment. Clinical staff involved in using the system provided positive feedback on the disease screening process, particularly praising the seamless workflow from patient registration to image transmission and obtaining the final result. Results from a questionnaire completed by 12 participants indicated that most found the system easy, quick, and highly satisfactory. The study also revealed room for improvement in the AMD model, suggesting the need to enhance its training data. Furthermore, the performance of the glaucoma model grading could be improved by incorporating additional measures such as intraocular pressure. CONCLUSIONS: The implementation of the AI-based approach for screening three chronic eye diseases proved effective in real-world settings, earning positive feedback on the usability of the integrated platform from both the screening staff and auditors. The auditing function has proven valuable for obtaining efficient second opinions from experts, pointing to its potential for enhancing remote screening capabilities. TRIAL REGISTRATION: Institutional Review Board of the Hamburg Medical Chamber (Ethik-Kommission der Ärztekammer Hamburg): 2021-10574-BO-ff.

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OBJECTIVE: To describe the peculiarities in imaging acquisition of fourteen patients with choroidal nevus using the Broad Line Fundus Imaging (BLFI) technology. METHODS: Single-center, retrospective, cross-sectional analysis. RESULTS: All images were acquired using the BLFI technology. We have found that choroidal nevus is undetectable in the blue channel (BC) (435-500 nm) and the green channel (GC) (500-585 nm). The only visible changes are related to the drusen, which appeared in BC and GC as light focal dots, correlated to the yellowish foci in the true-color image. On the red channel (RC) (585-640 nm), all lesions revealed the same pattern: a well-defined dark spot, with enhanced contrast, allowing the better visualization, measuring, and characterization of the nevus when compared with the other color channels, including the true-color imaging. CONCLUSION: BLFI application in choroidal nevus might be helpful at presentation, refining the diagnostic reliability, and monitoring, as it allows for better detection of alterations in the lesions. The peculiarities of the choroidal nevus are better assessed when using the RC due to its longer wavelength and deeper penetration in the retina and choroid.

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Hexokinase 1 (HK1) gene is the cause of autosomal dominant retinitis pigmentosa (RP) 79. To date, only E874K mutation has been reported as the causative mutation in patients with nonsyndromic RP. As a Caucasian RP case with a pathological variant of HK1 exhibiting pigmented paravenous retinochoroidal atrophy (PPRCA) phenotype was recently reported, we reviewed RP79 cases in our Japanese RP cohort. Consequently, 2 Japanese patients, who were diagnosed with RP79 by genetic tests in our RP cohort, were included in this study. Patient 1 was a 60-year-old woman. Fundus examination revealed symmetrical donut-shaped retinal degeneration, with pigment deposition avoiding the macula. Moreover, degeneration extended in a peripheral direction along the vessels like a starfish, and degeneration was observed around the veins and arteries. Patient 2 was a 75-year-old man. Fundus examination revealed symmetric macula-avoiding donut-shaped retinal degeneration, with paravenous protruding degeneration along the blood vessels like in case 1. Both Japanese cases, which belonged to two separate families, had the same HK1 pathogenic mutation, with a phenotype of PPRCA. Furthermore, atrophy along retinal arteries was noted. Reviewing previous nonsyndromic RP79 cases revealed symptoms that are believed to be those of PPRCA. Ultra-widefield fundus imaging, especially ultra-widefield fundus autofluorescence, has been useful in detecting PPRCA. If these devices become widely available, more cases may be discovered in the future because PPRCA can be used as a clue to suspect RP79, and Sanger sequencing may be used to identify pathogenic mutations in HK1 at a lower cost and more easily than using whole-exome sequencing.

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Background: The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special interest for primary health care services. Obtaining fundus imaging with this technique has improved and democratized the teaching of fundoscopy, but in particular, it contributes greatly to screening diseases with high rates of blindness. Eye examination using smartphones essentially represents a cheap and safe method, thus contributing to public policies on population screening. This review aims to provide an update on the use of this resource and its future prospects, especially as a screening and ophthalmic diagnostic tool. Methods: In this review, we surveyed major published advances in retinal and anterior segment analysis using AI. We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for published literature without a deadline. We included studies that compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting prevalent diseases with an accurate or commonly employed reference standard. Results: There are few databases with complete metadata, providing demographic data, and few databases with sufficient images involving current or new therapies. It should be taken into consideration that these are databases containing images captured using different systems and formats, with information often being excluded without essential detailing of the reasons for exclusion, which further distances them from real-life conditions. The safety, portability, low cost, and reproducibility of smartphone eye images are discussed in several studies, with encouraging results. Conclusions: The high level of agreement between conventional and a smartphone method shows a powerful arsenal for screening and early diagnosis of the main causes of blindness, such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration. In addition to streamlining the medical workflow and bringing benefits for public health policies, smartphone eye examination can make safe and quality assessment available to the population.

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It is well established that the retina provides insights beyond the eye. Through observation of retinal microvascular changes, studies have shown that the retina contains information related to cardiovascular disease. Despite the tremendous efforts toward reducing the effects of cardiovascular diseases, they remain a global challenge and a significant public health concern. Conventionally, predicting the risk of cardiovascular disease involves the assessment of preclinical features, risk factors, or biomarkers. However, they are associated with cost implications, and tests to acquire predictive parameters are invasive. Artificial intelligence systems, particularly deep learning (DL) methods applied to fundus images have been generating significant interest as an adjunct assessment tool with the potential of enhancing efforts to prevent cardiovascular disease mortality. Risk factors such as age, gender, smoking status, hypertension, and diabetes can be predicted from fundus images using DL applications with comparable performance to human beings. A clinical change to incorporate DL systems for the analysis of fundus images as an equally good test over more expensive and invasive procedures may require conducting prospective clinical trials to mitigate all the possible ethical challenges and medicolegal implications. This review presents current evidence regarding the use of DL applications on fundus images to predict cardiovascular disease.

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BACKGROUND: To develop deep learning models for medical diagnosis, it is important to collect more medical data from several medical institutions. Due to the regulations for privacy concerns, it is infeasible to collect data from various medical institutions to one institution for centralized learning. Federated Learning (FL) provides a feasible approach to jointly train the deep learning model with data stored in various medical institutions instead of collected together. However, the resulting FL models could be biased towards institutions with larger training datasets. METHODOLOGY: In this study, we propose the applicable method of Dynamically Synthetic Images for Federated Learning (DSIFL) that aims to integrate the information of local institutions with heterogeneous types of data. The main technique of DSIFL is to develop a synthetic method that can dynamically adjust the number of synthetic images similar to local data that are misclassified by the current model. The resulting global model can handle the diversity in heterogeneous types of data collected in local medical institutions by including the training of synthetic images similar to misclassified cases in local collections. RESULTS: In model performance evaluation metrics, we focus on the accuracy of each client's dataset. Finally, the accuracy of the model of DSIFL in the experiments can achieve the higher accuracy of the FL approach. CONCLUSION: In this study, we propose the framework of DSIFL that achieves improvements over the conventional FL approach. We conduct empirical studies with two kinds of medical images. We compare the performance by variants of FL vs. DSIFL approaches. The performance by individual training is used as the baseline, whereas the performance by centralized learning is used as the target for the comparison studies. The empirical findings suggest that the DSIFL has improved performance over the FL via the technique of dynamically synthetic images in training.

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Purpose/Background: We evaluate how a deep learning model can be applied to extract refractive error metrics from pupillary red reflex images taken by a low-cost handheld fundus camera. This could potentially provide a rapid and economical vision-screening method, allowing for early intervention to prevent myopic progression and reduce the socioeconomic burden associated with vision impairment in the later stages of life. Methods: Infrared and color images of pupillary crescents were extracted from eccentric photorefraction images of participants from Choithram Hospital in India and Dargaville Medical Center in New Zealand. The pre-processed images were then used to train different convolutional neural networks to predict refractive error in terms of spherical power and cylindrical power metrics. Results: The best-performing trained model achieved an overall accuracy of 75% for predicting spherical power using infrared images and a multiclass classifier. Conclusions: Even though the model's performance is not superior, the proposed method showed good usability of using red reflex images in estimating refractive error. Such an approach has never been experimented with before and can help guide researchers, especially when the future of eye care is moving towards highly portable and smartphone-based devices.

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Purpose: To present a technique for optimizing repair of traumatic retinal detachments (RDs). Methods: A patient was followed in an outpatient setting with clinical examinations, optical coherence tomography, widefield fundus photography, and anterior segment imaging. Results: The patient presented with a total RD after ruptured globe repair. The patient had combined corneal and retinal surgery with placement of a temporary keratoprosthesis (TKP) and vitrectomy with perfluorocarbon liquid (PFCL) to reattach the retina. The TKP and PFCL were left in the eye for 2 weeks before a planned silicone oil exchange and penetrating keratoplasty were performed. Four months postoperatively, the patient presented with a partially attached retina and improved vision. Conclusions: In this case, a ruptured globe decompensated several months after primary repair. The prolonged use of the TKP allowed for optimal surgical visualization, enhanced office-based assessment, and limited endothelial cell loss of the donor corneal tissue.