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

TL;DR

The TADPOLE Challenge assesses the ability of algorithms to predict the future progression of Alzheimer's disease using longitudinal data, aiming to improve early diagnosis and disease modeling.

Contribution

This paper introduces the design and framework of the TADPOLE Challenge for benchmarking Alzheimer's disease progression prediction models.

Findings

Participants trained models on ADNI data

Forecasted clinical diagnosis, ADAS-Cog13, and ventricle volume

Comparison of predictions with actual future measurements

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.