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Abstract INTRODUCTIONIdentification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODSWe applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews ofn = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTSOur best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI‐to‐AD progression within 6 years. DISCUSSIONThe proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy‐to‐administer screening tool for MCI‐to‐AD progression prediction, facilitating development of remote assessment. HighlightsVoice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment.The study leveraged AI methods for speech recognition and processed the resulting text using language models.The developed AI‐powered pipeline can lead to fully automated assessment that could enable remote and cost‐effective screening and prognosis for Alzehimer's disease.
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Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease
Abstract INTRODUCTIONAlzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. METHODSGiven the genetic susceptibility of AD, a multi‐factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk‐stratified groups, focusing on patients with mild cognitive impairment (MCI). RESULTSOur risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI,APOEε4– MCI, and amyloid+ MCI. DISCUSSIONOur risk score holds great potential to improve the precision of early risk assessment. HighlightsAccurate early risk assessment is critical for the success of clinical trials.A new risk score was built from integrating amyloid imaging and genetic data.Our risk score demonstrated improved capability in early risk stratification.
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- Award ID(s):
- 1942394
- PAR ID:
- 10542424
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Alzheimer's & Dementia
- Volume:
- 20
- Issue:
- 11
- ISSN:
- 1552-5260
- Format(s):
- Medium: X Size: p. 7819-7830
- Size(s):
- p. 7819-7830
- Sponsoring Org:
- National Science Foundation
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