Aljuhani Manal, Ashraf Azhaar, Edison Paul
Radiological Science and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Division of Neurology, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London W12 0NN, UK.
Cells. 2024 Nov 27;13(23):1965. doi: 10.3390/cells13231965.
Alzheimer's disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body dementia, and rare types of neurodegenerative diseases, including frontotemporal dementia. There is an unmet need to improve diagnosis and prognosis for patients with dementia, as cycles of misdiagnosis and diagnostic delays are challenging scenarios in neurodegenerative diseases. Neuroimaging is routinely used in clinical practice to support the diagnosis of neurodegenerative diseases. Clinical neuroimaging is amenable to errors owing to varying human judgement as the imaging data are complex and multidimensional. Artificial intelligence algorithms (machine learning and deep learning) enable automation of neuroimaging interpretation and may reduce potential bias and ameliorate clinical decision-making. Graph convolutional network-based frameworks implicitly provide multimodal sparse interpretability to support the detection of Alzheimer's disease and its prodromal stage, mild cognitive impairment. In patients with amyloid-related imaging abnormalities, radiologists had significantly better detection performances with both ARIA-E (sensitivity higher in the assisted/deep learning method [87%] compared to unassisted [71%]) and for ARIA-H signs (sensitivity was higher in assisted [79%] compared to unassisted [69%]). A convolutional neural network method was developed, and external validation predicted final clinical diagnoses of Alzheimer's disease, dementia with Lewy bodies, mild cognitive impairment due to Alzheimer's disease, or cognitively normal with FDG-PET. The translation of artificial intelligence to clinical practice is plagued with technical, disease-related, and institutional challenges. The implementation of artificial intelligence methods in clinical practice has the potential to transform the diagnostic and treatment landscape and improve patient health and outcomes.
阿尔茨海默病是老年人群（65岁及以上）痴呆症最常见的病因，其次是血管性痴呆、路易体痴呆以及包括额颞叶痴呆在内的罕见类型神经退行性疾病。改善痴呆症患者的诊断和预后存在未满足的需求，因为误诊和诊断延迟的循环在神经退行性疾病中是具有挑战性的情况。神经影像学在临床实践中常规用于支持神经退行性疾病的诊断。由于成像数据复杂且多维，临床神经影像学容易因不同的人为判断而出现错误。人工智能算法（机器学习和深度学习）能够实现神经影像学解释的自动化，并可能减少潜在偏差并改善临床决策。基于图卷积网络的框架隐含地提供多模态稀疏可解释性，以支持阿尔茨海默病及其前驱阶段轻度认知障碍的检测。在患有淀粉样蛋白相关成像异常的患者中，放射科医生对ARIA-E（辅助/深度学习方法的敏感性[87%]高于非辅助[71%]）和ARIA-H体征（辅助[79%]高于非辅助[69%]）的检测性能明显更好。开发了一种卷积神经网络方法，外部验证预测了阿尔茨海默病、路易体痴呆、阿尔茨海默病所致轻度认知障碍或FDG-PET认知正常的最终临床诊断。人工智能向临床实践的转化面临技术、疾病相关和机构方面的挑战。在临床实践中实施人工智能方法有可能改变诊断和治疗格局，并改善患者健康和治疗结果。
