Murugan Siva Raman Bala, Sanjay Srinivasan, Somanath Anjana, Mahendradas Padmamalini, Patil Aditya, Kaur Kirandeep, Gurnani Bharat
Department of Uveitis and Ocular Inflammation Uveitis Clinic, Aravind Eye Hospital, Pondicherry, 605007, India.
Department of Clinical Services, Singapore National Eye Centre, Third Hospital Ave, Singapore City, 168751, Singapore.
Clin Ophthalmol. 2024 Dec 14;18:3753-3766. doi: 10.2147/OPTH.S495307. eCollection 2024.
In the dynamic field of ophthalmology, artificial intelligence (AI) is emerging as a transformative tool in managing complex conditions like uveitis. Characterized by diverse inflammatory responses, uveitis presents significant diagnostic and therapeutic challenges. This systematic review explores the role of AI in advancing diagnostic precision, optimizing therapeutic approaches, and improving patient outcomes in uveitis care. A comprehensive search of PubMed, Scopus, Google Scholar, Web of Science, and Embase identified over 10,000 articles using primary and secondary keywords related to AI and uveitis. Rigorous screening based on predefined criteria reduced the pool to 52 high-quality studies, categorized into six themes: diagnostic support algorithms, screening algorithms, standardization of Uveitis Nomenclature (SUN), AI applications in management, systemic implications of AI, and limitations with future directions. AI technologies, including machine learning (ML) and deep learning (DL), demonstrated proficiency in anterior chamber inflammation detection, vitreous haze grading, and screening for conditions like ocular toxoplasmosis. Despite these advancements, challenges such as dataset quality, algorithmic transparency, and ethical concerns persist. Future research should focus on developing robust, multimodal AI systems and fostering collaboration among academia and industry to ensure equitable, ethical, and effective AI applications. The integration of AI heralds a new era in uveitis management, emphasizing precision medicine and enhanced care delivery.
在充满活力的眼科领域，人工智能（AI）正在成为管理葡萄膜炎等复杂病症的变革性工具。葡萄膜炎具有多种炎症反应，在诊断和治疗方面存在重大挑战。本系统综述探讨了人工智能在提高葡萄膜炎护理的诊断准确性、优化治疗方法以及改善患者预后方面的作用。通过对PubMed、Scopus、谷歌学术、科学网和Embase进行全面检索，使用与人工智能和葡萄膜炎相关的主要和次要关键词，共识别出10000多篇文章。根据预定义标准进行严格筛选后，最终纳入52项高质量研究，分为六个主题：诊断支持算法、筛查算法、葡萄膜炎命名标准化（SUN）、人工智能在管理中的应用、人工智能的系统影响以及局限性与未来方向。包括机器学习（ML）和深度学习（DL）在内的人工智能技术在检测前房炎症、玻璃体混浊分级以及筛查眼部弓形虫病等病症方面表现出了优势。尽管取得了这些进展，但数据集质量、算法透明度和伦理问题等挑战依然存在。未来的研究应专注于开发强大的多模式人工智能系统，并促进学术界和产业界之间的合作，以确保人工智能的公平、道德和有效应用。人工智能的整合预示着葡萄膜炎管理的新时代，强调精准医学和更好的护理服务。
