Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models

Accurate Alzheimer's Disease (AD) progression prediction is essential for early intervention. The TADPOLE challenge, involving 92 algorithms, used multimodal biomarkers to predict future clinical diagnosis, cognition, and ventricular volume. The winning algorithm, FROG, utilized a Longitudinal-to-Cross-sectional (L2C) transformation to convert variable longitudinal histories into fixed-length feature vectors, which contrasted with most existing approaches that fitted models to entire longitudinal histories, e.g., AD Course Map (AD-Map) and minimal recurrent neural networks (MinimalRNN). The TADPOLE challenge only utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. To evaluate FROG's generalizability, we trained it on the ADNI dataset and tested it on three external datasets covering 2,312 participants and 13,200 timepoints. We also introduced two FROG variants. One variant, L2C feedforward neural network (L2C-FNN), unified all XGBoost models used by the original FROG with an FNN. Across external datasets, L2C-FNN and AD-Map were the best for predicting cognition and ventricular volume. For clinical diagnosis prediction, L2C-FNN was the best, while AD-Map was the worst. L2C-FNN compared favorably with other approaches regardless of the number of observed timepoints, and when predicting from 0 to 6 years into the future, underscoring its potential for long-term dementia progression prediction. Pretrained ADNI models are publicly available: GITHUB_LINK.