Department of Ophthalmology.
Department of Neurology.
Curr Opin Ophthalmol. 2022 Sep 1;33(5):432-439. doi: 10.1097/ICU.0000000000000877. Epub 2022 Jul 12.
The aging world population accounts for the increasing prevalence of neurodegenerative diseases such as Alzheimer's and Parkinson's which carry a significant health and economic burden. There is therefore a need for sensitive and specific noninvasive biomarkers for early diagnosis and monitoring. Advances in retinal and optic nerve multimodal imaging as well as the development of artificial intelligence deep learning systems (AI-DLS) have heralded a number of promising advances of which ophthalmologists are at the forefront.
The association among retinal vascular, nerve fiber layer, and macular findings in neurodegenerative disease is well established. In order to optimize the use of these ophthalmic parameters as biomarkers, validated AI-DLS are required to ensure clinical efficacy and reliability. Varied image acquisition methods and protocols as well as variability in neurogenerative disease diagnosis compromise the robustness of ground truths that are paramount to developing high-quality training datasets.
In order to produce effective AI-DLS for the diagnosis and monitoring of neurodegenerative disease, multicenter international collaboration is required to prospectively produce large inclusive datasets, acquired through standardized methods and protocols. With a uniform approach, the efficacy of resultant clinical applications will be maximized.
老龄化世界人口导致神经退行性疾病（如阿尔茨海默病和帕金森病）的患病率不断上升，这些疾病给健康和经济带来了巨大负担。因此，需要敏感且特异的非侵入性生物标志物用于早期诊断和监测。视网膜和视神经多模态成像的进步以及人工智能深度学习系统（AI-DLS）的发展带来了许多有前途的进展，眼科医生处于这些进展的前沿。
神经退行性疾病中视网膜血管、神经纤维层和黄斑病变之间的关联已得到充分证实。为了优化这些眼科参数作为生物标志物的使用，需要经过验证的 AI-DLS 来确保临床功效和可靠性。不同的图像采集方法和协议以及神经退行性疾病诊断的差异会影响地面实况的稳健性，而地面实况对于开发高质量的训练数据集至关重要。
为了开发用于神经退行性疾病诊断和监测的有效 AI-DLS，需要国际多中心合作来前瞻性地生成大型综合数据集，这些数据集需要通过标准化的方法和协议来获取。通过统一的方法，可以最大限度地提高临床应用的效果。
