LoRA-MoE Deep Learning Framework for Alzheimer's Diagnosis via Handwriting
A new deep learning framework, known as Low-Rank Mixture of Experts (LoRA-MoE), has been introduced by researchers for diagnosing Alzheimer's disease (AD) through handwriting analysis. This method utilizes handwriting signals as a non-invasive and scalable digital biomarker that can detect subtle cognitive-motor impairments associated with the early stages of AD. The framework allows various experts to focus on distinct handwriting styles while utilizing a shared base network, enhancing efficiency and minimizing interference. Each expert incorporates lightweight low-rank adapters, which drastically reduce the number of trainable parameters compared to traditional Mixture of Experts models. This innovative approach seeks to facilitate early and accurate AD detection, aiding timely clinical interventions and the assessment of new therapies.
Key facts
- arXiv:2605.04079v1
- Low-Rank Mixture of Experts (LoRA-MoE) deep learning framework
- Alzheimer's disease diagnosis based on handwriting analysis
- Handwriting signals as non-invasive digital biomarker
- Captures subtle cognitive-motor impairments
- Multiple experts specialize in different handwriting patterns
- Low-rank adapters reduce trainable parameters
- Early detection for clinical intervention and therapy evaluation
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