TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease

Abstract

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting 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 or any other datasets to which they have access. Participants are then required to make monthly forecasts over a period of 5 years from January 2018, of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. These individual forecasts are later compared with the corresponding future measurements in ADNI-3 (obtained after the TADPOLE submission deadline). The first submission phase of TADPOLE was open for prize-eligible submissions between 15 June and 15 November 2017. The submission system remains open via the website: https://tadpole.grand-challenge.org, although since 15 November 2017 submissions are not eligible for the first round of prizes. This paper describes the design of the TADPOLE Challenge.

For more details on TADPOLE Challenge, see https://tadpole.grand-challenge.org/ This paper outlines the design of the TADPOLE Challenge. Paper contains 8 pages, 2 figures, 5 tables

References in corpus (1)

• A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging

Cited by in corpus (21)

• Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
• Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data
• Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
• Random forest prediction of Alzheimer's disease using pairwise selection from time series data
• Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial
• Differentiable Graph Module (DGM) for Graph Convolutional Networks
• TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data
• Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
• Latent-Graph Learning for Disease Prediction
• Using path signatures to predict a diagnosis of Alzheimer's disease
• AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers -- A narrative review of a growing field
• Multi-hop Convolutions on Weighted Graphs
• InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
• Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
• GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
• Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction
• MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks
• Longitudinal Deep Kernel Gaussian Process Regression
• Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy
• Multi-modal Graph Learning for Disease Prediction
• Machine Learning for Health: Personalized Models for Forecasting of Alzheimer Disease Progression