Dehghani Farzaneh, Derafshi Reihaneh, Lin Joanna, Bayat Sayeh, Bento Mariana
Biomedical Engineering Department, University of Calgary, Canada.
Computer Science Department, University of Calgary, Canada.
Yearb Med Inform. 2024 Aug;33(1):266-276. doi: 10.1055/s-0044-1800756. Epub 2025 Apr 8.
Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, resulting in progressive cognitive decline, and so accurate and timely AD diagnosis is of critical importance. To this end, various medical technologies and computer-aided diagnosis (CAD), ranging from biosensors and raw signals to medical imaging, have been used to provide information about the state of AD. In this survey, we aim to provide a review on CAD systems for automated AD detection, focusing on different data types: namely, signals and sensors, medical imaging, and electronic medical records (EMR).
We explored the literature on automated AD detection from 2022-2023. Specifically, we focused on various data resources and reviewed several preprocessing and learning methodologies applied to each data type, as well as evaluation metrics for model performance evaluation. Further, we focused on challenges, future perspectives, and recommendations regarding automated AD diagnosis.
Compared to other modalities, medical imaging was the most common data type. The prominent modality was Magnetic Resonance Imaging (MRI). In contrast, studies based on EMR data type were marginal because EMR is mostly used for AD prediction rather than detection. Several challenges were identified: data scarcity and bias, imbalanced datasets, missing information, anonymization, lack of standardization, and explainability.
Despite recent developments in automated AD detection, improving the trustworthiness and performance of these models, and combining different data types will improve usability and reliability of CAD tools for early AD detection in the clinical practice.
阿尔茨海默病（AD）是最常见的神经退行性疾病之一，会导致进行性认知衰退，因此准确及时的AD诊断至关重要。为此，从生物传感器和原始信号到医学成像等各种医学技术和计算机辅助诊断（CAD）已被用于提供有关AD状态的信息。在本次综述中，我们旨在对用于自动AD检测的CAD系统进行综述，重点关注不同的数据类型：即信号与传感器、医学成像以及电子病历（EMR）。
我们检索了2022年至2023年关于自动AD检测的文献。具体而言，我们聚焦于各种数据资源，回顾了应用于每种数据类型的几种预处理和学习方法，以及用于模型性能评估的指标。此外，我们还关注了自动AD诊断方面的挑战、未来展望和建议。
与其他模态相比，医学成像是最常见的数据类型。其中突出的模态是磁共振成像（MRI）。相比之下，基于EMR数据类型的研究较少，因为EMR大多用于AD预测而非检测。我们识别出了几个挑战：数据稀缺与偏差、数据集不平衡、信息缺失、匿名化、缺乏标准化以及可解释性。
尽管自动AD检测方面近来有所发展，但提高这些模型的可信度和性能，并结合不同数据类型，将提高CAD工具在临床实践中早期AD检测的可用性和可靠性。
