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With large-scale day-to-day data and the adoption of artificial intelligence, integrating AI and machine learning supports more precise and efficient detection of Alzheimer’s and other neurodegenerative diseases. Deep learning can emulate brain neural networks and, when applied to PET neuroimaging, help identify warning signs, Amyloid Beta plaques, TAU tangles, and brain degeneration to improve patient 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Opportunities Program |\n| --- | --- |\n| 2024\u003Cbr>Utilizing AI integrated neuroimaging technology to expand upon machine learning in positron emission tomography technology with the aim of detecting Amyloid Beta biomarkers early in the onset of Alzheimer's.\u003Cbr>Ethan S. Terman\u003Cbr>Virginia Commonwealth University\u003Cbr>Follow this and additional works at: [https://scholarscompass.vcu.edu/uresposters](https://scholarscompass.vcu.edu/uresposters)\u003Cbr> Part of the Bioinformatics Commons, Biotechnology Commons, Cellular and Molecular Physiology Commons, Cognitive Neuroscience Commons, Computational Neuroscience Commons, Developmental Neuroscience Commons, Investigative Techniques Commons, Medical Biomathematics and Biometrics Commons, Medical Neurobiology Commons, Molecular and Cellular Neuroscience Commons, Neurology Commons, Neurosciences Commons, Neurosurgery Commons, Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Other Psychiatry and Psychology Commons, Quality Improvement Commons, Surgical Procedures, Operative Commons, and the Therapeutics Commons\u003Cbr>© The Author(s) |  |\n\nDownloaded from  \nTerman, Ethan S., \"Utilizing AI integrated neuroimaging technology to expand upon machine learning in positron emission tomography technology with the aim of detecting Amyloid Beta biomarkers early in the onset of Alzheimer's.\" (2024) . Undergraduate Research Posters. Poster 433.  \n[https://scholarscompass.vcu.edu/uresposters/433](https://scholarscompass.vcu.edu/uresposters/433)  \nThis Book is brought to you for free and open access by the Undergraduate Research Opportunities Program at VCU Scholars Compass. It has been accepted for inclusion in Undergraduate Research Posters by an authorized administrator of VCU Scholars Compass. For more information, please contact [libcompass@vcu.edu](libcompass@vcu.edu).  \n[https://www.google.com/url?sa=i&u](https://www.google.com/url?sa=i&u)rl= [https%3A%2F%2Fpubs.rsna.org](https%3A%2F%2Fpubs.rsna.org)[ ](https%3A%2F%2Fpubs.rsna.org)%2Fdoi%2Ffull%2F10.1148%2Fradiol.2018181928&psig=AOvVaw3Fd 9zT-Ic8sIOkMCdUNG5m&ust=170 6045695248000&source=images&cd=vfe&opi=89978449&ved=0CBMQjRxqFwoTCNiH69X58YMDFQAAAAAdAAAAABAO  \nUtilizing AI integrated neuroimaging technology to expand upon machine learning in positron emission tomography technology with the aim of detecting Amyloid Beta biomarkers early in the onset of Alzheimer's.  \nEthan Terman  \nVIRGINIA COMMONWEALTH UNIVERSITY  \nAbstract  \nEarly intervention in Alzheimer's is vital for treatment. The earlier a professional can detect symptoms and make a diagnosis the earlier a prognosis can be implemented. With the prevalence of data in our  \nday-to-day world combined with Artificial intelligence (AI), utilizing both for machine learning can pave the way for more accurate and efficient detection of Alzheimer's and other neurodegenerative diseases. AI combined with Machine learning (ML) increases diagnostic efficiency and reduces human errors, making it a valuable resource for physicians and clinicians alike. With the increasing amount of data processing and image interpretation required, the ability to use AI and ML to augment and aid medical professionals will improve the quality of patient care. Deep learning algorithms and machine learning can emulate neural networks in the brain and can aid in the simulation of Alzheimer’s progression given the current data available. Applied to neuroimaging technology, trained machines will be able to detect warning signs earlier, locate Amyloid Beta plaques, TAU tangles, and detect degeneration in the brain.  \nIntroduction  \nAI methodologies are consistently being applied to almost every sector of society and increasingly in health care and medical research. AI combined with ML, which is a subset ofAI that utilizes data and algorithms to make predictions without oversight. Are early in","cbCaidcViwJWdgR0","https://ap.wps.com/l/cbCaidcViwJWdgR0","pdf",723065,"English","# Abstract\n## Introduction","[{\"question\":\"Why is early intervention important in Alzheimer’s research and treatment?\",\"answer\":\"Early detection of symptoms and diagnosis allows clinicians to implement a prognosis sooner, supporting more effective treatment planning.\"},{\"question\":\"How do AI and machine learning improve diagnostic efficiency in neurodegenerative diseases?\",\"answer\":\"They enhance diagnostic efficiency, reduce human error, and support faster image interpretation as data volume and processing demands grow.\"},{\"question\":\"What neuroimaging approach is targeted for biomarker detection in this work?\",\"answer\":\"The document focuses on AI/ML integrated with positron emission tomography (PET) to detect biomarkers such as Amyloid Beta plaques and TAU tangles earlier.\"}]","Utilizing AI integrated neuroimaging technology to expand upon machine learning in positron emission tomography technology - detect Amyloid Beta biomarkers early in Alzheimer's | PDF"]