ProMUSE: Progressive Multi-modal Uncertainty-guided Staged Evidential Alzheimer Disease Classification
arXiv:2606.19371v1 Announce Type: new Abstract: Alzheimer's disease (AD) is a fatal disorder that destroys memory and cognitive skills in the elderly population. Most treatments for AD are effective in the early stage, leading to an increasing demand for early AD diagnosis. AD diagnosis increasingly relies on multimodal data such as clinical assessments, structural Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET) imaging. However, MRI and PET acquisition remain costly and not universally accessible, making full-modality inference impractical in real-world clinical workf
The increasing availability and sophistication of AI in medical imaging analysis, coupled with the critical need for early disease detection, makes this a timely development.
This development has the potential to significantly improve the early diagnosis of Alzheimer's disease, leading to more effective early treatments and better patient outcomes.
The ability to accurately diagnose Alzheimer's with fewer, potentially less costly, modalities or in earlier stages with existing modalities alters the diagnostic landscape for this disease.
- · Patients with Alzheimer's disease
- · Healthcare providers
- · AI healthcare companies
- · Medical diagnostic companies
- · Traditional diagnostic methods reliant solely on expensive imaging
- · Companies slow to adopt AI in medical diagnostics
Improved early diagnosis of Alzheimer's disease with reduced reliance on expensive full-modality imaging.
Increased demand for AI-driven diagnostic tools and a potential shift in medical insurance coverage for these diagnostics.
Extension of proactive, preventative healthcare models for neurodegenerative diseases, possibly influencing pharmaceutical research and development priorities.
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Read at arXiv cs.LG