Ma Da, Pasquale Louis R, Girard Michaël J A, Leung Christopher K S, Jia Yali, Sarunic Marinko V, Sappington Rebecca M, Chan Kevin C
School of Medicine, Wake Forest University, Winston-Salem, NC, United States.
Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, NC, United States.
Front Ophthalmol (Lausanne). 2023;2. doi: 10.3389/fopht.2022.1057896. Epub 2023 Jan 4.
Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.
人工智能（AI）已被批准用于从床边临床研究到实验室基础科学研究等不同领域的生物医学研究。对于眼科研究，尤其是青光眼研究，鉴于可用的大量数据以及联邦学习的引入，人工智能应用在潜在的临床转化方面正在迅速发展。相反，尽管人工智能在提供机制性见解方面具有强大作用，但在基础科学中的应用仍然有限。从这个角度出发，我们讨论人工智能在青光眼科学发现应用中的最新进展、机遇和挑战。具体而言，我们关注反向翻译的研究范式，即首先将临床数据用于以患者为中心的假设生成，然后过渡到基础科学研究以验证假设。我们详细阐述了人工智能在青光眼反向翻译中几个独特的研究机会领域，包括疾病风险和进展预测、病理特征描述以及亚表型识别。我们最后总结了青光眼基础科学人工智能研究当前面临的挑战和未来机遇，如种间差异、人工智能模型的通用性和可解释性，以及使用先进眼部成像和基因组数据的人工智能应用。
