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Related Concept Videos

Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Artificial Intelligence-Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case

Han Wenzheng1, Edmund F Agyemang1, Sudesh K Srivastav1

  • 1Department of Biostatistics and Data Science, Celia Scott Weatherhead School of Public Health and Tropical Medicine at Tulane University, 1440 Canal St, New Orleans, LA, 70112, United States, 1 5049882475.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise for Alzheimer disease (AD) prediction. The SMART-Pred tool achieved 91% accuracy using handwriting analysis, offering a noninvasive early detection method.

Keywords:
AIAlzheimer diseaseSMART-PredShiny Multi-Algorithm R Tool for Predictive Modelingartificial intelligenceclassification algorithmsdisease diagnostics and surveillancemachine learningpredictive modeling

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Area of Science:

  • Neurology and Artificial Intelligence
  • Biomedical Informatics
  • Computational Neuroscience

Background:

  • Artificial intelligence (AI) demonstrates superior diagnostic accuracy in healthcare, with growing importance in medical practice.
  • SMART-Pred is an innovative AI-based application designed for Alzheimer disease (AD) prediction through handwriting analysis.

Purpose of the Study:

  • To develop and evaluate a noninvasive, cost-effective AI tool for early Alzheimer disease (AD) detection.
  • To address the need for accessible and accurate screening methods for AD.

Main Methods:

  • Utilized principal component analysis for dimensionality reduction of handwriting data.
  • Trained and evaluated 10 diverse AI models, including neural networks, on the DARWIN dataset (174 participants).
  • Assessed model performance using accuracy, sensitivity, specificity, and AUC metrics, incorporating explainable AI (Shapley Additive Explanations).

Main Results:

  • A neural network classifier achieved 91% accuracy and 94% AUC on the test set, surpassing current clinical diagnostic tools.
  • "Air_time" and "paper_time" consistently emerged as critical predictors for AD across all models.
  • The AI tool's performance aligns with recent advancements in AI-assisted AD prediction.

Conclusions:

  • SMART-Pred offers a noninvasive, cost-effective, and efficient method for AD prediction, showcasing AI's potential in healthcare.
  • Further clinical validation is needed, but findings support AI-assisted AD diagnosis for improved patient outcomes through early detection.