Publication Date
2-13-2025
Journal
Alzheimer's Research & Therapy
DOI
10.1186/s13195-025-01686-x
PMID
39948600
PMCID
PMC11823042
PubMedCentral® Posted Date
2-13-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Background: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treatment modes. We aimed to develop a deep learning model to predict cognitive conversion to guide re-assignment decisions to more intensive therapies where needed.
Methods: Longitudinal data including five variable sets, i.e. demographics, medical history, neuropsychological outcomes, laboratory and neuroimaging results, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were analyzed. We first developed a deep learning model to predicted cognitive conversion using all five variable sets. We then gradually removed variable sets to obtained parsimonious models for four different years of forecasting after baseline within acceptable frames of reduction in overall model fit (AUC remaining > 0.8).
Results: A total of 607 individuals were included at baseline, of whom 538 participants were followed up at 12 months, 482 at 24 months, 268 at 36 months and 280 at 48 months. Predictive performance was excellent with AUCs ranging from 0.87 to 0.92 when all variable sets were considered. Parsimonious prediction models that still had a good performance with AUC 0.80-0.84 were established, each only including two variable sets. Neuropsychological outcomes were included in all parsimonious models. In addition, biomarker was included at year 1 and year 2, imaging data at year 3 and demographics at year 4. Under our pre-set threshold, the rate of upgrade to more intensive therapies according to predicted cognitive conversion was always higher than according to actual cognitive conversion so as to decrease the false positive rate, indicating the proportion of patients who would have missed upgraded treatment based on prognostic models although they actually needed it.
Conclusions: Neurophysiological tests combined with other indicator sets that vary along the AD continuum can improve can provide aid for clinical treatment decisions leading to improved management of the disease.
Keywords
Humans, Alzheimer Disease, Deep Learning, Male, Female, Aged, Longitudinal Studies, Cognitive Dysfunction, Neuropsychological Tests, Disease Progression, Aged, 80 and over, Neuroimaging, Prognosis, Cohort Studies, Alzheimer’s disease, Machine learning, Cognitive conversion, Prediction model
Published Open-Access
yes
Recommended Citation
Yang, Siyu; Zhang, Xintong; Du, Xinyu; et al., "Prediction of Cognitive Conversion Within the Alzheimer’s Disease Continuum Using Deep Learning" (2025). Faculty and Staff Publications. 5677.
https://digitalcommons.library.tmc.edu/baylor_docs/5677
Included in
Allergy and Immunology Commons, Biological Phenomena, Cell Phenomena, and Immunity Commons, Pathology Commons
Comments
Trail registration information: ClinicalTrials.gov Identifier: NCT00106899 (Registration Date: 31 March 2005).