Applications In Ophthalmology Research Articles

Ethical considerations for large language models in ophthalmology.

This review aims to summarize and discuss the ethical considerations regarding large language model (LLM) use in the field of ophthalmology. This review of 47 articles on LLM applications in ophthalmology highlights their diverse potential uses, including education, research, clinical decision support, and surgical assistance (as an aid in operative notes). We also review ethical considerations such as the inability of LLMs to interpret data accurately, the risk of promoting controversial or harmful recommendations, and breaches of data privacy. These concerns imply the need for cautious integration of artificial intelligence in healthcare, emphasizing human oversight, transparency, and accountability to mitigate risks and uphold ethical standards. The integration of LLMs in ophthalmology offers potential advantages such as aiding in clinical decision support and facilitating medical education through their ability to process queries and analyze ophthalmic imaging and clinical cases. However, their utilization also raises ethical concerns regarding data privacy, potential misinformation, and biases inherent in the datasets used. Awareness of these concerns should be addressed in order to optimize its utility in the healthcare setting. More importantly, promoting responsible and careful use by consumers should be practiced.

A look at the emerging trends of large language models in ophthalmology.

As the surge in large language models (LLMs) and generative artificial intelligence (AI) applications in ophthalmology continue to expand, this review seeks to update physicians of the current progress, to catalyze further work to harness its capabilities to enhance healthcare delivery in ophthalmology. Generative AI applications have shown promising performance in Ophthalmology. Beyond native LLMs and question-answering based tasks, there has been increasing work in employing novel LLM techniques and exploring wider use case applications. In this review, we first look at existing LLM use case applications specific to Ophthalmology, followed by an overview of commonly used LLM techniques. We finally focus on the emerging trends of the generative AI space with an angle from ophthalmology.

Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review.

In recent years, ophthalmology has emerged as a new frontier in medical artificial intelligence (AI) with multi-modal AI in ophthalmology garnering significant attention across interdisciplinary research. This integration of various types and data models holds paramount importance as it enables the provision of detailed and precise information for diagnosing eye and vision diseases. By leveraging multi-modal ophthalmology AI techniques, clinicians can enhance the accuracy and efficiency of diagnoses, and thus reduce the risks associated with misdiagnosis and oversight while also enabling more precise management of eye and vision health. However, the widespread adoption of multi-modal ophthalmology poses significant challenges. In this review, we first summarize comprehensively the concept of modalities in the field of ophthalmology, the forms of fusion between modalities, and the progress of multi-modal ophthalmic AI technology. Finally, we discuss the challenges of current multi-modal AI technology applications in ophthalmology and future feasible research directions. In the field of ophthalmic AI, evidence suggests that when utilizing multi-modal data, deep learning-based multi-modal AI technology exhibits excellent diagnostic efficacy in assisting the diagnosis of various ophthalmic diseases. Particularly, in the current era marked by the proliferation of large-scale models, multi-modal techniques represent the most promising and advantageous solution for addressing the diagnosis of various ophthalmic diseases from a comprehensive perspective. However, it must be acknowledged that there are still numerous challenges associated with the application of multi-modal techniques in ophthalmic AI before they can be effectively employed in the clinical setting.

OphGLM: An ophthalmology large language-and-vision assistant

Vision computer-aided diagnostic methods have been used in early ophthalmic disease screening and diagnosis. However, the limited output formats of these methods lead to poor human–computer interaction and low clinical applicability value. Thus, ophthalmic visual question answering is worth studying. Unfortunately, no practical solutions exist before Large Language Models(LLMs). In this paper, we investigate the ophthalmic visual diagnostic interaction problem. We construct an ophthalmology large language-and-vision assistant, OphGLM, consisting of an image encoder, a text encoder, a fusion module, and an LLM module. We establish a new Chinese ophthalmic fine-tuning dataset, FundusTuning-CN, including the fundus instruction and conversation sets. Based on FundusTuning-CN, we establish a novel LLM-tuning strategy to introduce visual model understanding and ophthalmic knowledge into LLMs at a low cost and high efficiency. Leveraging the pre-training of the image encoder, OphGLM demonstrates strong visual understanding and surpasses open-source visual language models in common fundus disease classification tasks. The FundusTuning-CN enables OphGLM to surpass open-source medical LLMs in both ophthalmic knowledge and interactive capabilities. Our proposed OphGLM has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be publicly available at https://github.com/ML-AILab/OphGLM.

Gellan gum as a promising transplantation carrier for differentiated progenitor cells in ophthalmic therapies

Stem cell-based therapies for various ocular conditions are increasingly gaining traction in ophthalmic treatments, with hydrogel-based polymers playing a pivotal role. Current stem cell delivery methods face challenges such as limited cell retention, immunological rejection, and uneven dispersion. Hence, there is a critical demand for innovative delivery systems to enhance the viability, localization, and integration of transplanted stem cells while minimizing adverse effects. Central to this advancement is the meticulous selection of appropriate materials. Among the promising options, gellan gum, a versatile polysaccharide, is emerging as a potential carrier for differentiated progenitor cells in regenerative medicine, particularly in ophthalmology. This study explores the utilization of gellan gum hydrogels as carriers, focusing on their biocompatibility, customizable gelation properties, and ability to encapsulate, transplant, and biofunctionalize cells. Through a review of literature, the impact of gellan gum hydrogels on cell viability parameters is investigated, revealing their potential for promoting tissue regeneration and functional recovery in ocular diseases. Furthermore, this study compares gellan gum systems utilizing natural and synthetic polymers, discerning differences in efficacy, biocompatibility, and suitability for diverse applications in regenerative ophthalmology. This review highlights the promising role of gellan gum in ophthalmic therapies, providing valuable insights into future directions and hurdles in this evolving field.

Accuracy of a commonly used mobile ophthalmology application's vision assessment tools in measuring five vision assessment parameters.

The use of mobile ophthalmology applications (MOA) is increasing, but many of these tools have not been validated. This study was performed to assess the accuracy of a popular MOA, Eye Handbook, in measuring five commonly-tested vision assessment parameters (distance visual acuity (DVA), near visual acuity (NVA), colour vision testing (CVT), contrast sensitivity (CS), and pupillary distance (PD)) was compared with traditional vision assessment methods (TVAM) [i.e. Snellen chart, Rosenbaum near card, Ishihara, Pelli Robson test, etc.] performed in the eye clinic setting. Prospective crossover clinical trial of 129 patients meeting inclusion criteria. Participants averaged significantly better DVA (p = 0.0008), NVA (p < 0.0001), and CVT (p = 0.0105) in the MOA than the TVAM, but all three MOA assessments were predictive of the TVAM values. CS was significantly better with the MOA (p < 0.0001). Linear regression and Spearman correlation tests were applied to assess the effect of CS on NVA, which showed no clear relationship between the difference in NVA and the difference in CS. PD using the two methods was in agreement with no significant difference (p = 0.2889). The studied MOA offers an effective means of measuring four common vision parameters: DVA, NVA, CVT, and PD. The MOA can potentially be used by eye care providers, health care providers, and patients, both as a screening tool with correction factor and to monitor ocular pathologies. Atypical MOA measurements should prompt testing in the clinic with formal TVAMs.

Carbon quantum dots for the diagnosis and treatment of ophthalmic diseases.

Carbon quantum dots (CQDs), an emerging nanomaterial, are gaining attention in ophthalmological applications due to their distinctive physical, chemical, and biological characteristics. For example, their inherent fluorescent capabilities offer a novel and promising alternative to conventional fluorescent dyes for ocular disease diagnostics. Furthermore, because of the excellent biocompatibility and minimal cytotoxicity, CQDs are well-suited for therapeutic applications. In addition, functionalized CQDs can effectively deliver drugs to the posterior part of the eyeball to inhibit neovascularization. This review details the use of CQDs in the management of ophthalmic diseases, including various retinal diseases, and ocular infections. While still in its initial phases within ophthalmology, the significant potential of CQDs for diagnosing and treating eye conditions is evident.

Tunable Privacy Risk Evaluation of Generative Adversarial Networks.

Generative machine learning models such as Generative Adversarial Networks (GANs) have been shown to be especially successful in generating realistic synthetic data in image and tabular domains. However, it has been shown that such generative models, as well as the generated synthetic data, can reveal information contained in their privacy-sensitive training data, and therefore must be carefully evaluated before being used. The gold standard method through which such privacy leakage can be estimated is simulating membership inference attacks (MIAs), in which an attacker attempts to learn whether a given sample was part of the training data of a generative model. The state-of-the art MIAs against generative models, however, rely on strong assumptions (knowledge of the exact training dataset size), or require a lot of computational power (to retrain many "surrogate" generative models), which make them hard to use in practice. In this work, we propose a technique for evaluating privacy risks in GANs which exploits the outputs of the discriminator part of the standard GAN architecture. We evaluate our attacks in terms of performance in two synthetic image generation applications in radiology and ophthalmology, showing that our technique provides a more complete picture of the threats by performing worst-case privacy risk estimation and by identifying attacks with higher precision than the prior work.

Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.

Unwinding the Multi-Perspectives of Hyaluronic Acid as a Potent Cosmeceutical

The purpose of this study is to evaluate hyaluronic acid (HA), sometimes referred to as hyaluronan or hyaluronate, as well as its derivatives and how they are used in cosmetic formulations. N-acetylglucosamine and D-glucuronic acid are the two disaccharides that make up HA, a glycosaminoglycan that was first isolated from the vitreous humor of the eye and then found in other tissues and fluids, including the synovial fluid and articular cartilage. It is found in all vertebrates, including humans, and has a variety of roles in biological processes, such as wound healing, inflammation, cell differentiation, and embryological development. With its moisturizing and anti-aging properties, HA offers numerous advantages over other chemicals used in skin regeneration. The biological activity and skin penetration of HA are influenced by its molecular weight. The current study outlines the key elements of hyaluronic acid's usage in cosmetology, given its broad range of uses and applications in ophthalmology, arthrology, pneumology, rhinology, aesthetic medicine, cancer, nutrition, and cosmetics. There is a discussion of the biological impact of HA at the skin's level as well as any possible negative consequences. From the brand portfolios of the most well-known manufacturers, a few readily available cosmetic items containing HA have been discovered, and their composition has been assessed.

AI-based applications in ophthalmology: a discussion on future challenges in its use for diagnosis and treatment in the area

AI-based applications in ophthalmology: a discussion on future challenges in its use for diagnosis and treatment in the area

Ocular Disease Recognition Based on Deep Learning: A Comprehensive Review

This review article represents a major advance in the field of medical imaging and ophthalmology by exploring the critical role of deep learning in the detection and diagnosis of eye diseases. Early and accurate diagnosis becomes essential due to the frequency of ocular disorders that pose a significant risk to vision, including diabetic retinopathy, age-related macular degeneration, glaucoma, and cataracts. The need for more reliable automated solutions is highlighted by the limitations of traditional methods, despite their benefits, which include reliance on small datasets and manual feature analysis. Deep learning, a subset of machine learning, is becoming evident as a powerful tool that can interpret complex medical images and improve diagnostic accuracy without the need to extract human features. This article explores the evolution of deep learning applications in ophthalmology, highlighting the difficulties encountered such as interpretability of models and data quality and the creative solutions that have been found to overcome them. We highlight the revolutionary impact of deep learning in eye disease detection through an in-depth analysis of recent developments, providing insight into potential future research avenues that may further improve patient care in ophthalmology.

A Clinician's Guide to Sharing Data for AI in Ophthalmology.

Data is the cornerstone of using AI models, because their performance directly depends on the diversity, quantity, and quality of the data used for training. Using AI presents unique potential, particularly in medical applications that involve rich data such as ophthalmology, encompassing a variety of imaging methods, medical records, and eye-tracking data. However, sharing medical data comes with challenges because of regulatory issues and privacy concerns. This review explores traditional and nontraditional data sharing methods in medicine, focusing on previous works in ophthalmology. Traditional methods involve direct data transfer, whereas newer approaches prioritize security and privacy by sharing derived datasets, creating secure research environments, or using model-to-data strategies. We examine each method's mechanisms, variations, recent applications in ophthalmology, and their respective advantages and disadvantages. By empowering medical researchers with insights into data sharing methods and considerations, this review aims to assist informed decision-making while upholding ethical standards and patient privacy in medical AI development.

Challenges and prospects in the application of artificial intelligence for ocular disease screening and diagnosis

In recent years, artificial intelligence (AI) technologies have experienced substantial growth across various sectors, with significant strides made particularly in medical AI through advancements such as large models. The application of AI within the field of ophthalmology can enhance the accuracy of eye disease screening and diagnosis. However, the deployment of AI and its large models in ophthalmology still encounters numerous limitations and challenges. This article builds upon the transformative achievements in the medical AI sector and discusses the ongoing challenges faced by AI applications in ophthalmology. It provides forward-looking insights from an ophthalmic perspective regarding the era of large models and anticipates research trends in AI applications in ophthalmology, so as to foster the continuous advancement of AI technologies, thereby significantly promoting eye health.

In Vivo Ocular Pharmacokinetics and Toxicity of Siponimod in Albino Rabbits.

Siponimod is a promising agent for the inhibition of ocular neovascularization in diabetic retinopathy and age-related macular degeneration. Siponimod's development for ophthalmological application is hindered by the limited information available on the drug's solubility, stability, ocular pharmacokinetics (PK), and toxicity in vivo. In this study, we investigated the aqueous stability of siponimod under stress conditions (up to 60 °C) and its degradation behavior in solution. Additionally, siponimod's ocular PK and toxicity were investigated using intravitreal injection of two different doses (either 1300 or 6500 ng) in an albino rabbit model. Siponimod concentration was quantified in the extracted vitreous, and the PK parameters were calculated. The drug half-life after administration of the low and high doses was 2.8 and 3.9 h, respectively. The data obtained in vivo was used to test the ability of published in silico models to predict siponimod's PK accurately. Two models that correlated siponimod's molecular descriptors with its elimination from the vitreous closely predicted the half-life. Furthermore, 24 h and 7 days after intravitreal injections, the retinas showed no signs of toxicity. This study provides important information necessary for the formulation and development of siponimod for ophthalmologic applications. The short half-life of siponimod necessitates the development of a sustained drug delivery system to maintain therapeutic concentrations over an extended period, while the lack of short-term ocular toxicity observed in the retinas of siponimod-treated rabbits supports possible clinical use.

Unveiling the Potential: A Comprehensive Review of Artificial Intelligence Applications in Ophthalmology and Future Prospects.

Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in the field of ophthalmology. This comprehensive review examines the current applications of AI in ophthalmology, highlighting its significant contributions to diagnostic accuracy, treatment efficacy, and patient care. AI technologies, such as deep learning algorithms, have demonstrated exceptional performance in the early detection and diagnosis of various eye conditions, including diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Additionally, AI has enhanced the analysis of ophthalmic imaging techniques like optical coherence tomography (OCT) and fundus photography, facilitating more precise disease monitoring and management. The review also explores AI's role in surgical assistance, predictive analytics, and personalized treatment plans, showcasing its potential to revolutionize clinical practice and improve patient outcomes. Despite these advancements, challenges such as data privacy, regulatory hurdles, and ethical considerations remain. The review underscores the need for continued research and collaboration among clinicians, researchers, technology developers, and policymakers to address these challenges and fully harness the potential of AI in improving eye health worldwide. By integrating AI with teleophthalmology and developing AI-driven wearable devices, the future of ophthalmic care promises enhanced accessibility, efficiency, and efficacy, ultimately reducing the global burden of visual impairment and blindness.

Generalized 3D registration algorithm for enhancing retinal optical coherence tomography images.

Optical coherence tomography (OCT) has emerged as the standard of care for diagnosing and monitoring the treatment of various ocular disorders due to its noninvasive nature and in vivo volumetric acquisition capability. Despite its widespread applications in ophthalmology, motion artifacts remain a challenge in OCT imaging, adversely impacting image quality. While several multivolume registration algorithms have been developed to address this issue, they are often designed to cater to one specific OCT system or acquisition protocol. We aim to generate an OCT volume free of motion artifacts using a system-agnostic registration algorithm that is independent of system specifications or protocol. We developed a B-scan registration algorithm that removes motion and corrects for both translational eye movements and rotational angle differences between volumes. Tests were carried out on various datasets obtained from two different types of custom-built OCT systems and one commercially available system to determine the reliability of the proposed algorithm. Additionally, different system specifications were used, with variations in axial resolution, lateral resolution, signal-to-noise ratio, and real-time motion tracking. The accuracy of this method has further been evaluated through mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM). The results demonstrate improvements in the overall contrast of the images, facilitating detailed visualization of retinal vasculatures in both superficial and deep vasculature plexus. Finer features of the inner and outer retina, such as photoreceptors and other pathology-specific features, are discernible after multivolume registration and averaging. Quantitative analyses affirm that increasing the number of averaged registered volumes will decrease MSE and increase MS-SSIM as compared to the reference volume. The multivolume registered data obtained from this algorithm offers significantly improved visualization of the retinal microvascular network as well as retinal morphological features. Furthermore, we have validated that the versatility of our methodology extends beyond specific OCT modalities, thereby enhancing the clinical utility of OCT for the diagnosis and monitoring of ocular pathologies.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists.

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

A Two-Decade Bibliometric Analysis of Laser in Ophthalmology: From Past to Present.

Laser therapy has been proven as an effective technique for managing ophthalmological disorders. To guide future research, we conducted a bibliometric analysis of laser applications in eye diseases from 1990 to 2022, aiming to identify key themes and trends. We retrieved 3027 publications from the Web of Science Core Collection (WoSCC). Bibliometrix was used for science mapping of the literature, while VOSviewer and CiteSpace were applied to visualize co-authorship, co-citation, co-occurrence, and bibliographic coupling networks. From a co-citation reference network, we identified 52 distinct clusters. Our analysis uncovered three main research trends. The first trend revolves around the potential evolution of corneal laser surgery techniques, shifting from the treatment of refractive errors to broader applications in biomedical optics. The second trend illustrates the advancement of laser applications in treating a range of disorders, from retinal and ocular surface diseases to glaucoma. The third trend focuses on the innovative uses of established technologies. This study offers significant insights into the evolution of laser applications in ophthalmology over the past 30 years, which will undoubtedly assist scientists in directing further research in this promising field.

Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images.

Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for detection of retinal diseases, as well as other medical domains. The noise in OCT images presents a great challenge as it hinders the clinician's ability to diagnosis in extensive detail. In this work, a region-based, deep-learning, denoising framework is proposed for adaptive cleaning of noisy OCT-acquired images. The core of the framework is a hybrid deep-learning model named transformer enhanced autoencoder rendering (TEAR). Attention gates are utilized to ensure focus on denoising the foreground and to remove the background. TEAR is designed to remove the different types of noise artifacts commonly present in OCT images and to enhance the visual quality. Extensive quantitative evaluations are performed to evaluate the performance of TEAR and compare it against both deep-learning and traditional state-of-the-art denoising algorithms. The proposed method improved the peak signal-to-noise ratio to 27.9dB, CNR to 6.3dB, SSIM to 0.9, and equivalent number of looks to 120.8dB for a dental dataset. For a retinal dataset, the performance metrics in the same sequence are: 24.6, 14.2, 0.64, and 1038.7dB, respectively. The results show that the approach verifiably removes speckle noise and achieves superior quality over several well-known denoisers.