Opeyemi Adeniran, an AI researcher, has hinted on an AI-powered solution for early detection of Alzheimer’s disease through accurate MRI analysis.
According to a statement, she asserts that the innovation ensures accuracy, and builds clinician trust by making AI decisions more transparent.
The system integrates Convolutional Neural Networks (CNNs), Vision Transformers (ViT) and other architecture in an ensemble model, while embedding tools as LIME, Grad-CAM and Saliency Maps.
Addressing the “black box” problem in traditional AI systems, the framework allows clinicians to see not just the decision made, but also the reasoning behind it.
“This isn’t just about accuracy,” Adeniran said. “It’s about trust. Clinicians need to see why AI makes a decision, not just what the decision is.”
Testing showed good performance, with the stacking ensemble approach reaching 98 per cent accuracy and the hard voting method 97 per cent.
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Both approaches highlighted the hippocampal and temporal regions of the brain, areas linked to Alzheimer’s progression. This alignment with known medical markers reinforces the system’s reliability.
In addition, the framework classified patients into Cognitively Normal, Mild Cognitive Impairment and Alzheimer’s Disease categories, with minimal cases of misclassification.
She noted that such results show how AI aligns with clinical knowledge, providing evidence to trust and adopt the system in practice.
Alzheimer’s continues to present a growing public health challenge, currently affecting nearly seven million Americans. Estimates indicate that the number of cases can nearly double by 2060, making early detection more crucial than ever.
Detecting the disease at earlier stages provides doctors with a better opportunity to intervene and potentially slow its progression, thereby improving quality of life for patients.