Emanuele Tonti, Sofia Tonti, Flavia Mancini, Chiara Bonini, Leopoldo Spadea, Fabiana D'Esposito, Caterina Gagliano, Mutali Musa, Marco Zeppieri
{"title":"Artificial Intelligence and Advanced Technology in Glaucoma: A Review.","authors":"Emanuele Tonti, Sofia Tonti, Flavia Mancini, Chiara Bonini, Leopoldo Spadea, Fabiana D'Esposito, Caterina Gagliano, Mutali Musa, Marco Zeppieri","doi":"10.3390/jpm14101062","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions.</p><p><strong>Aim: </strong>This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions.</p><p><strong>Methods: </strong>A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included.</p><p><strong>Results: </strong>The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care.</p><p><strong>Conclusions: </strong>Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"14 10","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11508556/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Personalized Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/jpm14101062","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions.
Aim: This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions.
Methods: A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included.
Results: The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care.
Conclusions: Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.
背景：青光眼是导致全球不可逆失明的主要原因之一，因此需要针对患者个体特征制定精确的管理策略。人工智能（AI）有望通过提供个性化干预彻底改变青光眼治疗方法。目的：本综述探讨了目前人工智能在青光眼患者个性化管理中的应用情况，重点介绍了人工智能的进步、挑战和未来发展方向：对电子数据库（包括PubMed、Scopus和Web of Science）进行了系统检索，以确定截至2024年发表的相关研究。结果：综述发现了人工智能在青光眼患者个性化管理策略中的多种应用：综述发现了人工智能在青光眼管理中的各种应用，从早期检测和诊断到治疗优化和预后预测。机器学习算法，尤其是深度学习模型，在通过光学相干断层扫描（OCT）和视野测试等各种成像模式诊断青光眼方面表现出很高的准确性。人工智能驱动的风险分层工具通过将患者特定数据与预测分析相结合，促进了个性化治疗决策，在提高治疗效果的同时最大限度地减少了不良反应。此外，基于人工智能的远程眼科平台实现了远程监控和及时干预，提高了患者获得专业护理的机会：将人工智能技术整合到青光眼患者的个性化管理中，在优化临床决策、提高治疗效果和缓解疾病进展方面具有巨大潜力。然而，数据异质性、模型可解释性和监管问题等挑战值得进一步研究。未来的研究应侧重于完善人工智能算法，通过大规模前瞻性研究验证其临床实用性，并确保无缝集成到常规临床实践中，以实现个性化青光眼治疗的全部益处。
期刊介绍：
Journal of Personalized Medicine (JPM; ISSN 2075-4426) is an international, open access journal aimed at bringing all aspects of personalized medicine to one platform. JPM publishes cutting edge, innovative preclinical and translational scientific research and technologies related to personalized medicine (e.g., pharmacogenomics/proteomics, systems biology). JPM recognizes that personalized medicine—the assessment of genetic, environmental and host factors that cause variability of individuals—is a challenging, transdisciplinary topic that requires discussions from a range of experts. For a comprehensive perspective of personalized medicine, JPM aims to integrate expertise from the molecular and translational sciences, therapeutics and diagnostics, as well as discussions of regulatory, social, ethical and policy aspects. We provide a forum to bring together academic and clinical researchers, biotechnology, diagnostic and pharmaceutical companies, health professionals, regulatory and ethical experts, and government and regulatory authorities.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
