Pub Date : 2024-09-13eCollection Date: 2024-07-01DOI: 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。
{"title":"Big data and electronic health records for glaucoma research.","authors":"Isaac A Bernstein, Karen S Fernandez, Joshua D Stein, Suzann Pershing, Sophia Y Wang","doi":"10.4103/tjo.TJO-D-24-00055","DOIUrl":"10.4103/tjo.TJO-D-24-00055","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13eCollection Date: 2024-07-01DOI: 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.
{"title":"Big data for imaging assessment in glaucoma.","authors":"Douglas R da Costa, Felipe A Medeiros","doi":"10.4103/tjo.TJO-D-24-00079","DOIUrl":"10.4103/tjo.TJO-D-24-00079","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13eCollection Date: 2024-07-01DOI: 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.
{"title":"Investigating the comparative superiority of artificial intelligence programs in assessing knowledge levels regarding ocular inflammation, uvea diseases, and treatment modalities.","authors":"Eyupcan Sensoy, Mehmet Citirik","doi":"10.4103/tjo.TJO-D-23-00166","DOIUrl":"10.4103/tjo.TJO-D-23-00166","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>P</i> = 0.654).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13eCollection Date: 2024-07-01DOI: 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.
{"title":"Big data in visual field testing for glaucoma.","authors":"Alex T Pham, Annabelle A Pan, Jithin Yohannan","doi":"10.4103/tjo.TJO-D-24-00059","DOIUrl":"10.4103/tjo.TJO-D-24-00059","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488814/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13eCollection Date: 2024-07-01DOI: 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。
{"title":"Effectiveness of artificial intelligence for diabetic retinopathy screening in community in Binh Dinh Province, Vietnam.","authors":"Thanh Nguyen Van, Hoang Lan Vo Thi","doi":"10.4103/tjo.TJO-D-23-00101","DOIUrl":"10.4103/tjo.TJO-D-23-00101","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Materials and methods: </strong>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. <i>P</i> < 0.05 was considered statistically significant.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>EyeArt AI was effective for DR screening in community.</p>","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13eCollection Date: 2024-07-01DOI: 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.
{"title":"Application of artificial intelligence in glaucoma care: An updated review.","authors":"Jo-Hsuan Wu, Shan Lin, Sasan Moghimi","doi":"10.4103/tjo.TJO-D-24-00044","DOIUrl":"10.4103/tjo.TJO-D-24-00044","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13eCollection Date: 2024-07-01DOI: 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.
{"title":"Central visual field in glaucoma: An updated review.","authors":"Kelvin H Du, Alireza Kamalipour, Sasan Moghimi","doi":"10.4103/tjo.TJO-D-24-00042","DOIUrl":"10.4103/tjo.TJO-D-24-00042","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13eCollection Date: 2024-07-01DOI: 10.4103/tjo.TJO-D-24-00081
Shan Lin
{"title":"Advancing glaucoma care with big data and artificial intelligence innovations.","authors":"Shan Lin","doi":"10.4103/tjo.TJO-D-24-00081","DOIUrl":"https://doi.org/10.4103/tjo.TJO-D-24-00081","url":null,"abstract":"","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13eCollection Date: 2024-07-01DOI: 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.
{"title":"Artificial intelligence and big data integration in anterior segment imaging for glaucoma.","authors":"Sunee Chansangpetch, Mantapond Ittarat, Wisit Cheungpasitporn, Shan C Lin","doi":"10.4103/tjo.TJO-D-24-00053","DOIUrl":"10.4103/tjo.TJO-D-24-00053","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27eCollection Date: 2024-07-01DOI: 10.4103/tjo.TJO-D-23-00193
Jo-Hsuan Wu, Takashi Nishida, Sasan Moghimi, Robert N Weinreb
{"title":"Effects of prompt engineering on large language model performance in response to questions on common ophthalmic conditions.","authors":"Jo-Hsuan Wu, Takashi Nishida, Sasan Moghimi, Robert N Weinreb","doi":"10.4103/tjo.TJO-D-23-00193","DOIUrl":"10.4103/tjo.TJO-D-23-00193","url":null,"abstract":"","PeriodicalId":44978,"journal":{"name":"Taiwan Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}