Abstract
Alzheimer’s disease (AD) is a debilitating, irreversible neurological condition that leads to a severe decline in patients’ health, often resulting in fatality. Detecting AD and its early stages, such as mild cognitive impairment (MCI), which can manifest as stable (sMCI) or progressing (pMCI), is crucial for effective treatment planning and personalized therapy. Recent advancements in noninvasive retinal imaging technologies, including Optical Coherence Tomography (OCT), OCT angiography, and digital retinal photography, have enabled the examination of the neuronal and vascular structure of the retina in AD patients. Furthermore, the development of computer algorithms tailored to these imaging techniques has significantly enhanced AD research. This paper presents a comprehensive study on early AD identification that leverages state-of-the-art deep learning techniques and medical images or scans. It also explains the potential benefits of using emerging retinal scans for enhanced detection. It also explains various deep learning techniques that harness both local and global features to enhance accuracy by utilizing extensive scan data.
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Sami, N., Makkar, A., Meziane, F., Conway, M. (2024). Exploring Imaging Biomarkers for Early Detection of Alzheimer’s Disease Using Deep Learning: A Comprehensive Analysis. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2027. Springer, Cham. https://doi.org/10.1007/978-3-031-53085-2_17
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