通过视网膜成像在帕金森病中的人工智能应用
Artificial intelligence applications in Parkinson's disease via retinal imaging
AI总结：
本文研究了人工智能在视网膜成像中用于早期检测帕金森病及其严重程度预测的应用，展示了AI在视网膜血管结构分析中的高效性能。
摘要：
帕金森病（PD）由于人口老龄化而预计显著增加，因此早期诊断变得越来越重要，因为及时检测可能延缓进展并减少长期并发症。视网膜微血管已成为神经退行性变的有希望的解剖生物标志物，当与人工智能（AI）结合时，视网膜成像可能提供一种先进的、非侵入性的、成本效益高的筛查策略用于PD。本研究评估了过去35年中关于AI检测早期PD相关视网膜血管结构变化能力的证据。五个电子数据库包括PubMed、Web of Science、Scopus、ScienceDirect和ProQuest，从1990年1月到2025年1月进行了系统搜索。此外，Annals of Neurology和Frontiers in Neuroscience进行了手动搜索，参考列表中的纳入研究被筛查以寻找额外的合格出版物。十九项研究符合纳入标准。三种主要的诊断AI任务被识别出来，包括疾病分类、视网膜血管分割和PD风险分层。表现最好的模型是Drishti数据集上的ShAMBi-LSTM，准确率为97.2%，精确率为99.5%，灵敏度为96.9%，F1得分为0.981；nnU-Net准确率为99.7%，精确率为98.7%，灵敏度为98.9%，特异度为99.8%，Dice得分为98.9%；AlexNet用于风险预测，AUC值分别为0.77、0.68和0.73。总体而言，将AI算法应用于视网膜血管以检测PD的早期迹象并预测疾病严重程度的应用表明，AI与视网膜生物标志物的结合在早期和更准确的检测方面比传统临床评估单独使用具有巨大潜力。
英文摘要：
Parkinson's disease (PD) is projected to increase substantially due to population aging, making early diagnosis increasingly important, as timely detection may delay progression and reduce long-term complications. Retinal microvasculature has emerged as a promising anatomical biomarker of neurodegeneration, and when combined with artificial intelligence AI, retinal imaging may provide an advanced, noninvasive, and cost-effective screening strategy for PD. This study evaluated the evidence from the past 35 years regarding the capability of AI to detect early PD-related changes in retinal vascular structure. Five electronic databases including PubMed, Web of Science, Scopus, ScienceDirect, and ProQuest were systematically searched from January 1990 to January 2025. In addition, Annals of Neurology and Frontiers in Neuroscience were hand-searched, and the reference lists of included studies were screened for additional eligible publications. Nineteen studies met the inclusion criteria. Three principal diagnostic AI tasks were identified, including disease classification, retinal vessel segmentation, and PD risk stratification. The best-performing models were ShAMBi-LSTM on the Drishti dataset with 97.2 percent accuracy, 99.5 percent precision, 96.9 percent sensitivity, and an F1 score of 0.981 for classification, nnU-Net with 99.7 percent accuracy, 98.7 percent precision, 98.9 percent sensitivity, 99.8 percent specificity, and a Dice score of 98.9 percent for segmentation, and AlexNet for risk prediction with area under the curve values of 0.77, 0.68, and 0.73 across datasets. Overall, application of AI algorithms to retinal vasculature for detecting early signs of PD and predicting disease severity suggests that integration of AI with retinal biomarkers holds substantial potential for earlier and more accurate detection compared with traditional clinical evaluation alone.
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