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Early 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 day-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.

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AI, Neuroimaging, Machine Learning, Positron Emission Tomography (PET), Alzheimer's Disease, Amyloid Beta Biomarkers, Early Detection, Neurodegeneration Diagnostic Imaging


Bioinformatics | Biotechnology | Cellular and Molecular Physiology | Cognitive Neuroscience | Computational Neuroscience | Developmental Neuroscience | Investigative Techniques | Medical Biomathematics and Biometrics | Medical Neurobiology | Molecular and Cellular Neuroscience | Neurology | Neurosciences | Neurosurgery | Other Analytical, Diagnostic and Therapeutic Techniques and Equipment | Other Psychiatry and Psychology | Quality Improvement | Surgical Procedures, Operative | Therapeutics

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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.