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Updated: Jul 5, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
HOPE: Hybrid-Granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment
Abstract:
Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype labels, pMCI vs. stable MCI (sMCI), determined by whether or not an MCI patient will progress to AD after a long follow-up. However, prospectively acquiring MCI subtype data is time-consuming and resource-intensive; the resultant small datasets could lead to severe overfitting and difficulty in extracting discriminative information. Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction. First, HOPE learns an ordinal metric space that enables progression prediction by prototype comparison. Second, HOPE leverages a novel hybrid-granularity ordinal loss to learn the ordinal nature of AD via effectively integrating instance-to-instance ordinality, instance-to-class compactness, and class-to-class separation. Third, to make the prototype learning more stable, HOPE employs an exponential moving average strategy to learn the global prototypes of NC and AD dynamically. Experimental results on the internal ADNI and the external NACC datasets demonstrate the superiority of the proposed HOPE over existing state-of-the-art methods as well as its interpretability.
Insights
A new method called HOPE predicts Alzheimer's disease progression in mild cognitive impairment patients. It uses ordinal pathways from biomarkers, avoiding lengthy subtype labeling for better accuracy.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Machine Learning
Background:
- Mild cognitive impairment (MCI) frequently progresses to Alzheimer's disease (AD).
- Current methods for predicting MCI progression require time-consuming subtype labels (progressive MCI vs. stable MCI).
- Small datasets from long-term follow-ups lead to overfitting and hinder information extraction.
Purpose of the Study:
- To propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method for predicting MCI progression to AD.
- To characterize the ordinal progression of AD using longitudinal biomarkers and cognitive measurements.
- To overcome the limitations of existing methods requiring explicit MCI subtype labels.
Main Methods:
- HOPE learns an ordinal metric space for progression prediction via prototype comparison.
- A hybrid-granularity ordinal loss integrates instance-to-instance ordinality, instance-to-class compactness, and class-to-class separation.
- An exponential moving average strategy stabilizes prototype learning for normal cognition (NC) and AD.
Main Results:
- The HOPE method demonstrates superior performance over state-of-the-art methods on ADNI and NACC datasets.
- HOPE effectively characterizes the ordinal nature of AD progression.
- The method shows improved stability and interpretability in predicting MCI progression.
Conclusions:
- HOPE offers a more efficient and effective approach to predicting Alzheimer's disease progression from mild cognitive impairment.
- The method's ability to learn from ordinal pathways bypasses the need for extensive subtype labeling.
- HOPE presents a promising, interpretable tool for early AD risk assessment.
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