Sorrentino Francesco Saverio, Zeppieri Marco, Culiersi Carola, Florido Antonio, De Nadai Katia, Adamo Ginevra Giovanna, Pellegrini Marco, Nasini Francesco, Vivarelli Chiara, Mura Marco, Parmeggiani Francesco
Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy.
Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy.
Pharmaceuticals (Basel). 2024 Oct 28;17(11):1440. doi: 10.3390/ph17111440.
Neovascular age-related macular degeneration (nAMD) is one of the major causes of vision impairment that affect millions of people worldwide. Early detection of nAMD is crucial because, if untreated, it can lead to blindness. Software and algorithms that utilize artificial intelligence (AI) have become valuable tools for early detection, assisting doctors in diagnosing and facilitating differential diagnosis. AI is particularly important for remote or isolated communities, as it allows patients to endure tests and receive rapid initial diagnoses without the necessity of extensive travel and long wait times for medical consultations. Similarly, AI is notable also in big hubs because cutting-edge technologies and networking help and speed processes such as detection, diagnosis, and follow-up times. The automatic detection of retinal changes might be optimized by AI, allowing one to choose the most effective treatment for nAMD. The complex retinal tissue is well-suited for scanning and easily accessible by modern AI-assisted multi-imaging techniques. AI enables us to enhance patient management by effectively evaluating extensive data, facilitating timely diagnosis and long-term prognosis. Novel applications of AI to nAMD have focused on image analysis, specifically for the automated segmentation, extraction, and quantification of imaging-based features included within optical coherence tomography (OCT) pictures. To date, we cannot state that AI could accurately forecast the therapy that would be necessary for a single patient to achieve the best visual outcome. A small number of large datasets with high-quality OCT, lack of data about alternative treatment strategies, and absence of OCT standards are the challenges for the development of AI models for nAMD.
新生血管性年龄相关性黄斑变性（nAMD）是全球数百万人视力受损的主要原因之一。nAMD的早期检测至关重要，因为如果不进行治疗，可能会导致失明。利用人工智能（AI）的软件和算法已成为早期检测的宝贵工具，可协助医生进行诊断并促进鉴别诊断。AI对偏远或孤立社区尤为重要，因为它使患者无需长途跋涉和长时间等待医疗咨询就能接受检查并获得快速初步诊断。同样，AI在大城市中心也很显著，因为前沿技术和网络有助于加快检测、诊断和随访等流程。AI可以优化视网膜变化的自动检测，从而为nAMD选择最有效的治疗方法。复杂的视网膜组织非常适合扫描，并且可通过现代AI辅助多成像技术轻松获取。AI使我们能够通过有效评估大量数据来加强患者管理，促进及时诊断和长期预后。AI在nAMD方面的新应用主要集中在图像分析上，特别是用于光学相干断层扫描（OCT）图像中基于成像特征的自动分割、提取和量化。迄今为止，我们不能说AI能够准确预测单个患者为实现最佳视觉效果所需的治疗方法。少量高质量OCT的大型数据集、缺乏替代治疗策略的数据以及OCT标准的缺失是开发nAMD人工智能模型面临的挑战。
