Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review of Deep Learning Models for Early-Stage Disease Classification.
Machine learning models using brain imaging and blood markers identify people with mild memory loss likely to develop Alzheimer's disease within years.
This systematic review evaluates ML models for predicting MCI-to-AD conversion, finding several models with robust predictive accuracy using multimodal biomarker inputs. The maturity of evidence suggests near-term clinical translation for early intervention targeting in the pre-dementia window.
What the study was
- Study design
- Systematic review (ML models for MCI-to-AD conversion prediction)
- Population
- Adults with mild cognitive impairment
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- Ageing research reviews
Why it surfaced
Systematic review of ML-based early AD prediction with validated performance; near-term implementable for pre-dementia intervention targeting in the rapidly growing global aging population.
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