ArticleAlzheimer's research & therapy2025
Prediction of cognitive conversion within the Alzheimer's disease continuum using deep learning.
Article in Alzheimer's research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00106899 (Alzheimer's Disease Neuroimaging Initiative), which is not on this map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Alzheimer's Disease Neuroimaging Initiative
Who cites it
4 citing papers in PubMed.
- Prediction of alzheimer's disease time to dementia onset using cross-sectional data from spatiotemporal biomarker progression patterns.European journal of nuclear medicine and molecular imaging · 2026Article
- Longitudinal multi-modal data prediction model for mild cognitive impairment by deep survival analysis.BMC medical informatics and decision making · 2026Article
- Diffusion Basis Restricted Fraction as a Putative Magnetic Resonance Imaging Marker of Neuroinflammation: Histological Evidence, Diagnostic Accuracy, and Translational Potential.Life (Basel, Switzerland) · 2025Review
- Deep multimodal learning for domain-level cognitive decline prediction in Alzheimer's disease.Frontiers in artificial intelligence · 2025Article
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Authors and funding
17 authors.
Funding
Abstract
backgroundEarly 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.
methodsLongitudinal 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).
resultsA 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.
conclusionsNeurophysiological 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. TRAIL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier: NCT00106899 (Registration Date: 31 March 2005).
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