Kumar Rahul, Waisberg Ethan, Ong Joshua, Paladugu Phani, Amiri Dylan, Saintyl Jeremy, Yelamanchi Jahnavi, Nahouraii Robert, Jagadeesan Ram, Tavakkoli Alireza
Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1600 NW 10th Ave, Miami, FL 33136, USA.
Department of Clinical Neurosciences, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK.
Brain Sci. 2024 Dec 17;14(12):1266. doi: 10.3390/brainsci14121266.
Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer's disease, Parkinson's disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques-Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging-in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and inflammation. Additionally, next-generation PET tracers targeting misfolded proteins, such as tau and alpha-synuclein, along with inflammatory markers, enhance the visualization and quantification of pathological processes in vivo. Deep learning models, including convolutional neural networks and multimodal transformers, further improve diagnostic accuracy by integrating multimodal imaging data and predicting disease progression. Despite challenges such as technical variability, data privacy concerns, and regulatory barriers, the potential of AI-enhanced neuroimaging to revolutionize early diagnosis and personalized treatment in neurodegenerative and neuro-ophthalmic disorders is immense. This review underscores the importance of ongoing efforts to validate, standardize, and implement these technologies to maximize their clinical impact.
神经影像学的进展，特别是扩散磁共振成像（MRI）技术和正电子发射断层扫描（PET）分子成像，显著提高了神经退行性疾病和神经眼科疾病生物标志物的早期检测能力。这些疾病包括阿尔茨海默病、帕金森病、多发性硬化症、视神经脊髓炎和髓鞘少突胶质细胞糖蛋白抗体病。本综述强调了先进的扩散MRI技术——神经突方向离散度与密度成像和扩散峰度成像——在识别临床症状出现之前大脑和视觉通路中细微的微观结构变化方面的变革性作用。当与人工智能（AI）算法相结合时，这些技术实现了前所未有的诊断精度，有助于早期发现神经退行性变和炎症。此外，针对错误折叠蛋白（如tau蛋白和α-突触核蛋白）以及炎症标志物的新一代PET示踪剂，增强了体内病理过程的可视化和量化。深度学习模型，包括卷积神经网络和多模态变压器，通过整合多模态成像数据和预测疾病进展，进一步提高了诊断准确性。尽管存在技术变异性、数据隐私问题和监管障碍等挑战，但人工智能增强神经影像学在神经退行性疾病和神经眼科疾病中彻底改变早期诊断和个性化治疗的潜力是巨大的。本综述强调了持续努力验证、标准化和实施这些技术以最大化其临床影响的重要性。
