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Big data and electronic health records for glaucoma research. 用于青光眼研究的大数据和电子健康记录。
IF 1 Q4 OPHTHALMOLOGY Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI: 10.4103/tjo.TJO-D-24-00055
Isaac A Bernstein, Karen S Fernandez, Joshua D Stein, Suzann Pershing, Sophia Y Wang

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.

通过电子病历(EHR)实现的健康记录数字化改变了眼科研究的面貌,尤其是在青光眼研究方面。电子病历提供了大量结构化和非结构化数据,可对患者特征、治疗史和治疗结果进行全面分析。本综述全面讨论了不同的电子病历数据来源、其优势、局限性以及对青光眼研究的适用性。机构电子病历库提供详细的多模态临床数据,有助于对青光眼等疾病进行深入研究,并促进人工智能应用的开发。视力结果研究合作组织(Sight Outcomes Research Collaborative)和视力智能研究登记处(Intelligent Research In Sight registry)等多中心计划提供了更大、更多样化的数据集,提高了研究结果的可推广性,并为有关青光眼流行病学、治疗结果和实践模式的大规模研究提供了支持。我们所有人研究计划特别强调多样性和包容性,通过纳入代表性不足的人群和提供电子病历以外的全面健康数据,为青光眼研究提供了一个独特的机会。挑战依然存在,如数据访问限制和标准化问题,但可以通过研究人员、机构和监管机构之间的持续合作来解决。标准化的数据格式和改进的数据链接方法,尤其是眼科成像和检测方面的数据链接方法,将进一步提高电子病历数据集在眼科研究中的实用性,最终在全球范围内促进我们对青光眼和其他眼科疾病的了解和治疗。
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引用次数: 0
Big data for imaging assessment in glaucoma. 用于青光眼成像评估的大数据。
IF 1 Q4 OPHTHALMOLOGY Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI: 10.4103/tjo.TJO-D-24-00079
Douglas R da Costa, Felipe A Medeiros

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.

青光眼是导致全球不可逆转性失明的主要原因,许多人直到晚期才意识到自己的病情,导致严重的视野损伤。尽管有有效的治疗方法,但预计到 2040 年,将有超过 1.1 亿人患有青光眼。早期发现和可靠监测对防止视力丧失至关重要。随着计算技术的快速发展,人工智能(AI)和深度学习(DL)算法正在成为筛查、诊断和监测青光眼进展的潜在工具。利用庞大的数据源,这些技术有望通过更早地发现疾病、预测病情发展和深入了解潜在机制来提高临床实践和公共卫生成果。本综述评估了大数据和人工智能在青光眼研究中的应用,概述了最相关的主题,讨论了用于筛查、诊断、监测疾病进展、关联结构和功能变化、评估图像质量以及探索生成式人工智能等创新技术的各种模型。
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引用次数: 0
Investigating the comparative superiority of artificial intelligence programs in assessing knowledge levels regarding ocular inflammation, uvea diseases, and treatment modalities. 研究人工智能程序在评估眼部炎症、葡萄膜疾病和治疗方法相关知识水平方面的比较优势。
IF 1 Q4 OPHTHALMOLOGY Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI: 10.4103/tjo.TJO-D-23-00166
Eyupcan Sensoy, Mehmet Citirik

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.

目的:本研究的目的是评估 Chat Generative Pretrained Transformer(ChatGPT)、Bard 和 Bing 人工智能(AI)聊天机器人对眼部炎症、葡萄膜疾病和治疗方式的了解程度,并研究它们之间的相对性能比较:向 ChatGPT、Bard 和 Bing 人工智能聊天机器人提出了 36 个与眼部炎症、葡萄膜疾病和治疗方式有关的问题,并记录了正确和错误的回答。使用卡方检验比较了正确率:结果:ChatGPT 提供了 52.8% 的正确答案,Bard 回答了 38.9% 的正确答案,Bing 回答了 44.4% 的正确答案。所有三个人工智能程序都对 20 个问题(55.6%)做出了相同的回答,其中 45% 回答正确,55% 回答错误。三个人工智能聊天机器人的正确回答和错误回答之间没有明显差异(P = 0.654):结论:应开发人工智能聊天机器人,以广泛提供有关眼部炎症、葡萄膜疾病和治疗方法的准确信息。未来的研究可以探索提高这些聊天机器人性能的方法。
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引用次数: 0
Big data in visual field testing for glaucoma. 青光眼视野测试中的大数据。
IF 1 Q4 OPHTHALMOLOGY Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI: 10.4103/tjo.TJO-D-24-00059
Alex T Pham, Annabelle A Pan, Jithin Yohannan

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.

近年来,医疗保健领域的技术进步和不断增长的数据库推动了 "大数据 "分析法的出现。大数据有可能彻底改变医疗保健行业,尤其是眼科,因为眼科是数据密集型的医学专业。青光眼是导致全球不可逆转性失明的主要原因之一,它是一种眼科疾病,在眼科护理领域的创新发展中备受关注。青光眼最重要的数据来源之一是视野(VF)检测,它是诊断和管理该疾病的基石。随着大型视野数据库可访问性的不断扩大,调查大数据分析在青光眼中的各种应用的研究激增。在本研究中,我们回顾了大数据在以下方面的应用:评估VF测试的可靠性、深入了解真实世界的临床实践和结果、了解新的疾病关联和风险因素、描述VF丧失的模式、定义青光眼的结构-功能关系、加强早期诊断或更早发现病情发展、为临床决策提供信息以及改进临床试验。同样重要的是,我们将讨论当前大数据分析面临的挑战和未来的改进方向。
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引用次数: 0
Effectiveness of artificial intelligence for diabetic retinopathy screening in community in Binh Dinh Province, Vietnam. 人工智能在越南平定省社区糖尿病视网膜病变筛查中的应用效果。
IF 1 Q4 OPHTHALMOLOGY Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI: 10.4103/tjo.TJO-D-23-00101
Thanh Nguyen Van, Hoang Lan Vo Thi

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.

目的:本研究旨在评估人工智能(AI)在越南平定省社区糖尿病视网膜病变(DR)筛查中的灵敏度、特异性和准确性:这项回顾性、描述性、横断面研究通过分析平定省医院和医疗中心 583 名糖尿病患者的 2332 张非眼动数字眼底照片,评估了 EyeArt 系统 v2.0 的 DR 筛查效果。首先,我们选取了 30 名患者的 120 张数字眼底照片,由两名眼科医生进行 Kappa 指数分析,他们将负责 DR 临床特征评估和 DR 严重程度分级。其次,对所有数字眼底照片进行编码,然后送交上述两位眼科医生,由他们根据国际眼科委员会的指南进行评估和分类。最后,将 EyeArt 的 DR 严重程度量表与眼科医生的量表进行比较,作为 EyeArt 效果的参考标准。所有数据均使用 SPSS 软件 20.0 版进行分析。灵敏度、特异性、阳性预测值、阴性预测值和准确性的数值(置信区间为 95%)根据 DR 状态、可转诊与否和是否威胁视力的 DR 状态进行计算。P<0.05为有统计学意义:EyeArt筛查DR的灵敏度和特异度分别为94.1%和87.2%。可转诊 DR 和视力受威胁 DR 的灵敏度和特异度分别为 96.6%、90.1% 和 100.0%、92.2%。DR筛查、可转诊DR和视力受威胁DR的准确率分别为88.9%、91.4%和93.0%:结论:EyeArt AI 对社区 DR 筛查有效。
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引用次数: 0
Application of artificial intelligence in glaucoma care: An updated review. 人工智能在青光眼护理中的应用:最新综述。
IF 1 Q4 OPHTHALMOLOGY Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI: 10.4103/tjo.TJO-D-24-00044
Jo-Hsuan Wu, Shan Lin, Sasan Moghimi

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.

人工智能(AI)在眼科领域的应用在过去十年中得到了越来越多的探索。大量研究结果表明,人工智能在改善眼科疾病管理方面大有可为,青光眼也不例外。青光眼是一种不可逆的视力疾病,起病隐匿,病理生理学复杂,需要长期治疗。由于青光眼的临床治疗仍面临各种挑战,人工智能在促进青光眼治疗方面的潜在作用受到了广泛关注。在本研究中,我们回顾了近年来发表的研究人工智能在青光眼管理中应用的相关文献。将讨论的人工智能应用的主要方面包括青光眼风险预测、青光眼检测和诊断、视野估计和模式分析、青光眼进展检测以及其他应用。
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引用次数: 0
Central visual field in glaucoma: An updated review. 青光眼的中心视野:最新综述。
IF 1 Q4 OPHTHALMOLOGY Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI: 10.4103/tjo.TJO-D-24-00042
Kelvin H Du, Alireza Kamalipour, Sasan Moghimi

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.

对青光眼患者的中心视力进行评估非常重要,因为这影响到患者的生活质量和日常生活活动,如阅读、驾驶和行走。10-2 视野(VF)评估仍然是青光眼诊断和进展过程中中心视力功能分析的主要方法。然而,在临床实践中,这一方法可能未得到充分利用。在监测疾病进展(尤其是晚期病例)、评估某些眼部疾病(如高度近视、视盘出血、低角膜滞后和某些视盘表型)的青光眼以及更早地检测中心视野损伤等情况下,使用 10-2 模式进行额外监测可为常用的 24-2 模式提供补充性临床信息。此外,人工智能技术的发展可以帮助临床医生识别更多中心性 VF 损伤的风险因素,从而最有效地分配有限的资源。在本研究中,我们旨在确定更有可能造成中心 VF 损伤的特定患者特征,并评估在各种临床环境中采用 10-2 VF 的益处。
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引用次数: 0
Advancing glaucoma care with big data and artificial intelligence innovations. 利用大数据和人工智能创新推进青光眼治疗。
IF 1 Q4 OPHTHALMOLOGY Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI: 10.4103/tjo.TJO-D-24-00081
Shan Lin
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引用次数: 0
Artificial intelligence and big data integration in anterior segment imaging for glaucoma. 青光眼前节成像中的人工智能和大数据整合。
IF 1 Q4 OPHTHALMOLOGY Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI: 10.4103/tjo.TJO-D-24-00053
Sunee Chansangpetch, Mantapond Ittarat, Wisit Cheungpasitporn, Shan C Lin

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.

人工智能(AI)和大数据在眼前节(AS)成像中的整合代表了一种变革性的青光眼诊断和管理方法。本文探讨了各种青光眼成像技术,如青光眼光学相干断层扫描、超声生物显微镜和测角摄影,强调了它们在识别闭角型青光眼疾病中的作用。综述重点介绍了人工智能的进步,包括机器学习和深度学习,它们增强了图像分析并使青光眼治疗中的复杂过程自动化,还提供了这些技术的性能和临床应用方面的最新证据。此外,文章还讨论了大数据的整合问题,详细介绍了大数据通过实现综合数据分析、促进临床决策和推动个性化治疗策略,为医学成像带来革命性变化的潜力。在这篇文章中,我们探讨了标准化和整合不同数据集所面临的挑战,并提出未来的合作和技术进步将大大改善青光眼的管理和研究。这篇对当前证据和新技术的综述强调了它们的临床相关性,并深入探讨了它们改变传统青光眼评估和治疗方法的潜力。
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引用次数: 0
Effects of prompt engineering on large language model performance in response to questions on common ophthalmic conditions. 在回答有关常见眼科疾病的问题时,提示工程对大语言模型性能的影响。
IF 1 Q4 OPHTHALMOLOGY Pub Date : 2024-08-27 eCollection Date: 2024-07-01 DOI: 10.4103/tjo.TJO-D-23-00193
Jo-Hsuan Wu, Takashi Nishida, Sasan Moghimi, Robert N Weinreb
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引用次数: 0
期刊
Taiwan Journal of Ophthalmology
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