Taiwan Journal of Ophthalmology最新文献

The digitization of health records through electronic health records (EHRs) has transformed the landscape of ophthalmic research, particularly in the study of glaucoma. EHRs offer a wealth of structured and unstructured data, allowing for comprehensive analyses of patient characteristics, treatment histories, and outcomes. This review comprehensively discusses different EHR data sources, their strengths, limitations, and applicability towards glaucoma research. Institutional EHR repositories provide detailed multimodal clinical data, enabling in-depth investigations into conditions such as glaucoma and facilitating the development of artificial intelligence applications. Multicenter initiatives such as the Sight Outcomes Research Collaborative and the Intelligent Research In Sight registry offer larger, more diverse datasets, enhancing the generalizability of findings and supporting large-scale studies on glaucoma epidemiology, treatment outcomes, and practice patterns. The All of Us Research Program, with a special emphasis on diversity and inclusivity, presents a unique opportunity for glaucoma research by including underrepresented populations and offering comprehensive health data even beyond the EHR. Challenges persist, such as data access restrictions and standardization issues, but may be addressed through continued collaborative efforts between researchers, institutions, and regulatory bodies. Standardized data formats and improved data linkage methods, especially for ophthalmic imaging and testing, would further enhance the utility of EHR datasets for ophthalmic research, ultimately advancing our understanding and treatment of glaucoma and other ocular diseases on a global scale.

Glaucoma is the leading cause of irreversible blindness worldwide, with many individuals unaware of their condition until advanced stages, resulting in significant visual field impairment. Despite effective treatments, over 110 million people are projected to have glaucoma by 2040. Early detection and reliable monitoring are crucial to prevent vision loss. With the rapid development of computational technologies, artificial intelligence (AI) and deep learning (DL) algorithms are emerging as potential tools for screening, diagnosing, and monitoring glaucoma progression. Leveraging vast data sources, these technologies promise to enhance clinical practice and public health outcomes by enabling earlier disease detection, progression forecasting, and deeper understanding of underlying mechanisms. This review evaluates the use of Big Data and AI in glaucoma research, providing an overview of most relevant topics and discussing various models for screening, diagnosis, monitoring disease progression, correlating structural and functional changes, assessing image quality, and exploring innovative technologies such as generative AI.

Purpose: The purpose of the study was to evaluate the knowledge level of the Chat Generative Pretrained Transformer (ChatGPT), Bard, and Bing artificial intelligence (AI) chatbots regarding ocular inflammation, uveal diseases, and treatment modalities, and to investigate their relative performance compared to one another.

Materials and methods: Thirty-six questions related to ocular inflammation, uveal diseases, and treatment modalities were posed to the ChatGPT, Bard, and Bing AI chatbots, and both correct and incorrect responses were recorded. The accuracy rates were compared using the Chi-squared test.

Results: The ChatGPT provided correct answers to 52.8% of the questions, while Bard answered 38.9% correctly, and Bing answered 44.4% correctly. All three AI programs provided identical responses to 20 (55.6%) of the questions, with 45% of these responses being correct and 55% incorrect. No significant difference was observed between the correct and incorrect responses from the three AI chatbots (P = 0.654).

Conclusion: AI chatbots should be developed to provide widespread access to accurate information about ocular inflammation, uveal diseases, and treatment modalities. Future research could explore ways to enhance the performance of these chatbots.

Recent technological advancements and the advent of ever-growing databases in health care have fueled the emergence of "big data" analytics. Big data has the potential to revolutionize health care, particularly ophthalmology, given the data-intensive nature of the medical specialty. As one of the leading causes of irreversible blindness worldwide, glaucoma is an ocular disease that receives significant interest for developing innovations in eye care. Among the most vital sources of data in glaucoma is visual field (VF) testing, which stands as a cornerstone for diagnosing and managing the disease. The expanding accessibility of large VF databases has led to a surge in studies investigating various applications of big data analytics in glaucoma. In this study, we review the use of big data for evaluating the reliability of VF tests, gaining insights into real-world clinical practices and outcomes, understanding new disease associations and risk factors, characterizing the patterns of VF loss, defining the structure-function relationship of glaucoma, enhancing early diagnosis or earlier detection of progression, informing clinical decisions, and improving clinical trials. Equally important, we discuss current challenges in big data analytics and future directions for improvement.

The application of artificial intelligence (AI) in ophthalmology has been increasingly explored in the past decade. Numerous studies have shown promising results supporting the utility of AI to improve the management of ophthalmic diseases, and glaucoma is of no exception. Glaucoma is an irreversible vision condition with insidious onset, complex pathophysiology, and chronic treatment. Since there remain various challenges in the clinical management of glaucoma, the potential role of AI in facilitating glaucoma care has garnered significant attention. In this study, we reviewed the relevant literature published in recent years that investigated the application of AI in glaucoma management. The main aspects of AI applications that will be discussed include glaucoma risk prediction, glaucoma detection and diagnosis, visual field estimation and pattern analysis, glaucoma progression detection, and other applications.

Purpose: The objective of this study is to evaluate the sensitivity, specificity, and accuracy of artificial intelligence (AI) for diabetic retinopathy (DR) screening in community in Binh Dinh Province in Vietnam.

Materials and methods: This retrospective, descriptive, cross-sectional study assessed the DR screening efficacy of EyeArt system v2.0 by analyzing 2332 nonmydriatic digital fundus pictures of 583 diabetic patients from hospitals and health centers in Binh Dinh province. First, we selected thirty patients with 120 digital fundus pictures to perform the Kappa index by two eye doctors who would be responsible for the DR clinical feature evaluation and DR severity scale classification. Second, all digital fundus pictures were coded and then sent to the two above-mentioned eye doctors for the evaluation and classifications according to the International Committee of Ophthalmology's guidelines. Finally, DR severity scales with EyeArt were compared with those by eye doctors as a reference standard for EyeArt's effectiveness. All the data were analyzed using the SPSS software version 20.0. Values (with confidence interval 95%) of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated according to DR state, referable or not and vision-threatening DR state or not. P < 0.05 was considered statistically significant.

Results: The sensitivity and specificity of EyeArt for DR screening were 94.1% and 87.2%. The sensitivity and specificity for referable DR and vision-threatening DR were 96.6%, 90.1%, and 100.0%, 92.2%. Accuracy for DR screening, referable DR, and vision-threatening DR were 88.9%, 91.4%, and 93.0%, respectively.

Conclusion: EyeArt AI was effective for DR screening in community.

Evaluation of central vision in glaucoma is important due to its impact on patients' quality of life and activities of daily living such as reading, driving, and walking. The 10-2 visual field (VF) assessment remains a mainstay in the functional analysis of central vision in glaucoma diagnosis and progression. However, it may be underutilized in clinical practice. Monitoring of disease progression especially in advanced cases, glaucoma evaluation in certain ocular disorders such as high myopia, disc hemorrhage, low corneal hysteresis, and certain optic disc phenotypes, as well as earlier detection of central VF damage, are certain conditions where additional monitoring with the 10-2 pattern may provide complementary clinical information to the commonly utilized 24-2 pattern. In addition, the development of artificial intelligence techniques may assist clinicians to most effectively allocate limited resources by identifying more risk factors to central VF damage. In this study, we aimed to determine specific patient characteristics that make central VF damage more likely and to assess the benefit of incorporating the 10-2 VF in various clinical settings.

The integration of artificial intelligence (AI) and big data in anterior segment (AS) imaging represents a transformative approach to glaucoma diagnosis and management. This article explores various AS imaging techniques, such as AS optical coherence tomography, ultrasound biomicroscopy, and goniophotography, highlighting their roles in identifying angle-closure diseases. The review focuses on advancements in AI, including machine learning and deep learning, which enhance image analysis and automate complex processes in glaucoma care, and provides current evidence on the performance and clinical applications of these technologies. In addition, the article discusses the integration of big data, detailing its potential to revolutionize medical imaging by enabling comprehensive data analysis, fostering enhanced clinical decision-making, and facilitating personalized treatment strategies. In this article, we address the challenges of standardizing and integrating diverse data sets and suggest that future collaborations and technological advancements could substantially improve the management and research of glaucoma. This synthesis of current evidence and new technologies emphasizes their clinical relevance, offering insights into their potential to change traditional approaches to glaucoma evaluation and care.