Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning
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 shi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/9/4184 |
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| author | Aristidis G. Vrahatis Konstantina Skolariki Marios G. Krokidis Konstantinos Lazaros Themis P. Exarchos Panagiotis Vlamos |
| author_facet | Aristidis G. Vrahatis Konstantina Skolariki Marios G. Krokidis Konstantinos Lazaros Themis P. Exarchos Panagiotis Vlamos |
| author_sort | Aristidis G. Vrahatis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:08:07Z |
| format | Article |
| id | doaj.art-931bf82d2a934f21a8e5ee696756418b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:08:07Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-931bf82d2a934f21a8e5ee696756418b2023-11-17T23:40:50ZengMDPI AGSensors1424-82202023-04-01239418410.3390/s23094184Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep LearningAristidis G. Vrahatis0Konstantina Skolariki1Marios G. Krokidis2Konstantinos Lazaros3Themis P. Exarchos4Panagiotis Vlamos5Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, GreeceBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, GreeceBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, GreeceBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, GreeceBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, GreeceBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, GreeceAlzheimer’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.https://www.mdpi.com/1424-8220/23/9/4184Alzheimer’s diseaseearly predictionnon-invasive biomarkersexplainable AIdeep learning |
| spellingShingle | Aristidis G. Vrahatis Konstantina Skolariki Marios G. Krokidis Konstantinos Lazaros Themis P. Exarchos Panagiotis Vlamos Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning Sensors Alzheimer’s disease early prediction non-invasive biomarkers explainable AI deep learning |
| title | Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning |
| title_full | Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning |
| title_fullStr | Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning |
| title_full_unstemmed | Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning |
| title_short | Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning |
| title_sort | revolutionizing the early detection of alzheimer s disease through non invasive biomarkers the role of artificial intelligence and deep learning |
| topic | Alzheimer’s disease early prediction non-invasive biomarkers explainable AI deep learning |
| url | https://www.mdpi.com/1424-8220/23/9/4184 |
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