acePredicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review of Deep Learning Models for Early-Stage Disease Classification
Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder for which early diagnosis-particularly the accurate prediction of conversion from mild cognitive impairment (MCI) to AD-is essential to enable timely and effective therapeutic interventions. Deep learning (DL) models have demonstrated substantial promise in this domain; however, critical challenges persist, including multiclass staging of disease progression, longitudinal data modeling, and effective multimodal data integration. Th…
Deep learning models showed promising and often strong performance for predicting conversion from mild cognitive impairment to Alzheimer's disease, supporting early diagnosis and timely therapeutic intervention.
Deep learning models for MCI-to-AD conversion face substantial barriers to routine clinical use due to heavy reliance on ADNI, lack of diverse multicenter data, overfitting, and poor interpretability.
Findings are constrained by heavy reliance on a single dataset, methodological heterogeneity preventing direct comparison, and common issues of overfitting and poor interpretability.
Evidence
- Peer-reviewedAgeing Research Reviews2026-09-11
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Truvace Impact Record TRV-2026-1067, v1: “Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review of Deep Learning Models for Early-Stage Disease Classification.” Truvace, 2026-09-13. /record/TRV-2026-1067 (accessed at citation time). sha256 e45a05e7d395866b…
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