Hashemian Hesam, Peto Tunde, Ambrósio Renato, Lengyel Imre, Kafieh Rahele, Muhammed Noori Ahmed, Khorrami-Nejad Masoud
Translational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.
School of Medicine, Dentistry and Biomedical Sciences, Centre for Public Health, Queen's University Belfast, Northern Ireland, UK.
J Ophthalmic Vis Res. 2024 Sep 16;19(3):354-367. doi: 10.18502/jovr.v19i3.15893. eCollection 2024 Jul-Sep.
Artificial intelligence (AI) holds immense promise for transforming ophthalmic care through automated screening, precision diagnostics, and optimized treatment planning. This paper reviews recent advances and challenges in applying AI techniques such as machine learning and deep learning to major eye diseases. In diabetic retinopathy, AI algorithms analyze retinal images to accurately identify lesions, which helps clinicians in ophthalmology practice. Systems like IDx-DR (IDx Technologies Inc, USA) are FDA-approved for autonomous detection of referable diabetic retinopathy. For glaucoma, deep learning models assess optic nerve head morphology in fundus photographs to detect damage. In age-related macular degeneration, AI can quantify drusen and diagnose disease severity from both color fundus and optical coherence tomography images. AI has also been used in screening for retinopathy of prematurity, keratoconus, and dry eye disease. Beyond screening, AI can aid treatment decisions by forecasting disease progression and anti-VEGF response. However, potential limitations such as the quality and diversity of training data, lack of rigorous clinical validation, and challenges in regulatory approval and clinician trust must be addressed for the widespread adoption of AI. Two other significant hurdles include the integration of AI into existing clinical workflows and ensuring transparency in AI decision-making processes. With continued research to address these limitations, AI promises to enable earlier diagnosis, optimized resource allocation, personalized treatment, and improved patient outcomes. Besides, synergistic human-AI systems could set a new standard for evidence-based, precise ophthalmic care.
人工智能（AI）在通过自动筛查、精准诊断和优化治疗方案来改变眼科护理方面具有巨大潜力。本文综述了将机器学习和深度学习等人工智能技术应用于主要眼部疾病的最新进展和挑战。在糖尿病视网膜病变中，人工智能算法分析视网膜图像以准确识别病变，这有助于眼科临床医生的实践。像IDx-DR（美国IDx科技公司）这样的系统已获得美国食品药品监督管理局（FDA）批准，可自主检测可转诊的糖尿病视网膜病变。对于青光眼，深度学习模型通过评估眼底照片中的视神经乳头形态来检测损伤。在年龄相关性黄斑变性中，人工智能可以量化玻璃膜疣，并从彩色眼底图像和光学相干断层扫描图像中诊断疾病严重程度。人工智能还被用于早产儿视网膜病变、圆锥角膜和干眼症的筛查。除了筛查，人工智能还可以通过预测疾病进展和抗血管内皮生长因子（anti-VEGF）反应来辅助治疗决策。然而，要广泛应用人工智能，必须解决一些潜在的局限性，如训练数据的质量和多样性、缺乏严格的临床验证以及监管审批和临床医生信任方面的挑战。另外两个重大障碍包括将人工智能集成到现有的临床工作流程中，以及确保人工智能决策过程的透明度。随着持续研究以解决这些局限性，人工智能有望实现更早诊断、优化资源分配、个性化治疗并改善患者预后。此外，人机协同系统可能为基于证据的精准眼科护理树立新的标准。
