Trinh Han, Vice Jordan, Tajbakhsh Zahra, Charng Jason, Alam Khyber, Chen Fred K, Mian Ajmal
Department of Optometry, School of Health and Clinical Sciences, The University of Western Australia, 39 Fairway, Crawley, 6009, Western Australia, Australia.
School of Physics, Maths and Computing, Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, Perth, 6009, Western Australia, Australia.
Biomed Phys Eng Express. 2025 Jul 16;11(4). doi: 10.1088/2057-1976/ade9c7.
Inherited retinal diseases (IRDs) are a diverse group of genetic disorders that lead to progressive vision loss and are a major cause of blindness in working-age adults. The complexity and heterogeneity of IRDs pose significant challenges in diagnosis, prognosis, and management. Recent advancements in artificial intelligence (AI) offer promising solutions to these challenges. However, the rapid development of AI techniques and their varied applications have led to fragmented knowledge in this field. This review consolidates existing studies, identifies gaps, and provides an overview of AI's potential in diagnosing and managing IRDs. It aims to structure pathways for advancing clinical applications by exploring AI techniques like machine learning and deep learning, particularly in disease detection, progression prediction, and personalized treatment planning. Additionally, the integration of explainable AI is discussed, emphasizing its importance in clinical settings to improve transparency and trust in AI-based systems. The review addresses the need to bridge existing gaps in focused studies on AI's role in IRDs, offering a structured analysis of current AI techniques and outlining future research directions. It concludes with an overview of the challenges and opportunities in deploying AI for IRDs, highlighting the need for interdisciplinary collaboration and the continuous development of robust, interpretable AI models to advance clinical applications.
遗传性视网膜疾病（IRDs）是一组多样的遗传性疾病，可导致进行性视力丧失，是工作年龄成年人失明的主要原因。IRDs的复杂性和异质性在诊断、预后和管理方面带来了重大挑战。人工智能（AI）的最新进展为这些挑战提供了有前景的解决方案。然而，AI技术的快速发展及其多样的应用导致了该领域知识的碎片化。本综述整合了现有研究，找出差距，并概述了AI在诊断和管理IRDs方面的潜力。它旨在通过探索机器学习和深度学习等AI技术，构建推进临床应用的途径，特别是在疾病检测、进展预测和个性化治疗规划方面。此外，还讨论了可解释AI的整合，强调其在临床环境中的重要性，以提高对基于AI系统的透明度和信任度。本综述满足了弥合关于AI在IRDs中作用的现有重点研究差距的需求，对当前AI技术进行了结构化分析，并概述了未来的研究方向。它最后概述了将AI应用于IRDs的挑战和机遇，强调了跨学科合作的必要性以及持续开发强大、可解释AI模型以推进临床应用的需求。
