ArticlePredictive Intelligence in Medicine. PRIME (Workshop)2019
TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data.
Article in Predictive Intelligence in Medicine. PRIME (Workshop), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer's Disease: A Systematic Review and Meta-Analysis.Frontiers in aging neuroscience · 2022Pooled it
- FastEBM: Fast, Scalable, and Uncertainty-Aware Event-Based Disease Progression Modeling.bioRxiv : the preprint server for biology · 2026Article
- Robust Longitudinal Dementia Prediction under Systemic Missingness via Hierarchical Fusion and Test-Time Adaptation.medRxiv : the preprint server for health sciences · 2026Article
- A Generalized Responsible AI Framework for Trustworthy Clinical Prediction: Explainability, Fairness, Performance, and Uncertainty in Alzheimer's Disease Modeling.Healthcare (Basel, Switzerland) · 2026Article
- AutoML-Multiverse: An Instability-Aware Framework for Quantifying Analytic Variability in Alzheimer's Disease Machine-Learning Studies.medRxiv : the preprint server for health sciences · 2026Article
- Deep learning to predict future cognitive decline: a multimodal approach using brain MRI and clinical data.Frontiers in neuroimaging · 2026Article
- Learning Explainable Imaging-Genetics Associations Related to a Neurological Disorder.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Survey on sampling conditioned brain images and imaging measures with generative models.Biomedical engineering letters · 2025Review
- Prediction-powered inference for clinical trials: application to linear covariate adjustment.BMC medical research methodology · 2025Article
- Prediction-powered Inference for Clinical Trials: application to linear covariate adjustment.medRxiv : the preprint server for health sciences · 2025Article
- Cross-Dataset Evaluation of Dementia Longitudinal Progression Prediction Models.Human brain mapping · 2025Article
- Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models.medRxiv : the preprint server for health sciences · 2025Article
- Comprehensive Anatomical Staging Predicts Clinical Progression in Mild Cognitive Impairment: A Data-Driven Approach.International journal of molecular sciences · 2025Article
- MRI Distance Measures as a Predictor of Subsequent Clinical Status During the Preclinical Phase of Alzheimer's Disease and Related Disorders.Human brain mapping · 2025Article
- Deep multimodal learning for domain-level cognitive decline prediction in Alzheimer's disease.Frontiers in artificial intelligence · 2025Article
- Augmenting radiological assessment of imaging evident dementias with radiomic analysis.NPJ dementia · 2025Article
- Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies.Brain sciences · 2024Review
- Implementation and validation of face de-identification (de-facing) in ADNI4.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Prediction of future dementia among patients with mild cognitive impairment (MCI) by integrating multimodal clinical data.Heliyon · 2024Article
Corrections and comments
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11 authors.
Funding
Abstract
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog 13), and total volume of the ventricles - which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time of forecasting (it was acquired afterwards), and that it focuses on the challenging problem of cohort selection for clinical trials by identifying fast progressors. The submission phase of TADPOLE was open until 15 November 2017; since then data has been acquired until April 2019 from 219 subjects with 223 clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used for the evaluation of the participants' predictions. Thirty-three teams participated with a total of 92 submissions. No single submission was best at predicting all three outcomes. For diagnosis prediction, the best forecast (team Frog), which was based on gradient boosting, obtained a multiclass area under the receiver-operating curve (MAUC) of 0.931, while for ventricle prediction the best forecast (team
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