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• W4366828958 endingPage "4184" @default.
• W4366828958 startingPage "4184" @default.
• W4366828958 abstract "Alzheimer’s disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era." @default.
• W4366828958 created "2023-04-25" @default.
• W4366828958 creator A5001357889 @default.
• W4366828958 creator A5002406655 @default.
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• W4366828958 creator A5057894101 @default.
• W4366828958 creator A5069517572 @default.
• W4366828958 creator A5082119404 @default.
• W4366828958 date "2023-04-22" @default.
• W4366828958 modified "2023-10-16" @default.
• W4366828958 title "Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning" @default.
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• W4366828958 doi "https://doi.org/10.3390/s23094184" @default.

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