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Updated: Aug 5, 2026
06:46
Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
Published on: August 4, 2018
Adaptive cascading artificial intelligence for Alzheimer's disease assessment: a clinically oriented narrative review
Sedighe Hooshmandi1, Khojaste Rahimi Jaberi2, Firuz Kamalov3
1Department of Radiology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Summary
Artificial intelligence (AI) shows promise for Alzheimer's disease (AD) diagnosis, but clinical use is limited. A new AI framework using blood biomarkers and progressive screening offers a practical path for equitable dementia care.
Area of Science:
- Neurology
- Artificial Intelligence
- Biomarkers
Background:
- AI models demonstrate high accuracy in Alzheimer's disease (AD) diagnosis using multi-modal data (neuroimaging, CSF, genetics, cognitive tests).
- Clinical adoption of AI for AD diagnosis is hindered by the inaccessibility of specialized diagnostic tools and idealized research settings.
- Advancements in blood-based biomarkers (plasma phosphorylated tau, GFAP, NfL) offer minimally invasive diagnostic opportunities.
Purpose of the Study:
- To propose a practical framework for translating AI benchmark performance into real-world dementia care pathways.
- To introduce a clinically grounded AI-assisted cascading model for scalable and equitable dementia care.
- To address challenges in AI implementation for dementia diagnosis and care.
Main Methods:
- Development of a cascading AI model mirroring clinical workflows: progressive screening, biomarker-guided assessment, selective imaging, and prognostic monitoring.
- Integration of advanced blood-based biomarkers for enhanced diagnostic flexibility.
- Application of enabling AI methods: sequential decision-making, reinforcement learning, cost-sensitive learning, missing-modality robustness, and explainable AI.
Main Results:
- The proposed model facilitates a progressive, biomarker-guided approach to dementia diagnosis and care.
- The framework enhances the practical utility of AI by aligning with real-world clinical pathways.
- Addresses limitations of current AI systems by incorporating cost-effectiveness and accessibility.
Conclusions:
- AI-assisted cascading models, leveraging blood biomarkers, can bridge the gap between AI performance and clinical dementia care.
- Scalable, equitable, and clinically deployable AI solutions are crucial for improving dementia diagnosis and management.
- Future work should focus on data design, validation, healthcare integration, and ethical considerations for AI in dementia care.
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The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ and tau...
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