A Novel Tabular-to-Image Conversion Approach for Frontotemporal Dementia Progression Forecasting using Hybrid CNN-RNN Models

Frontotemporal Dementia (FTD) presents significant challenges in disease progression forecasting due to its complex temporal dynamics and the logistical difficulties of acquiring longitudinal imaging data. Convolutional Neural Networks (CNNs) excel in image-based tasks but struggle with tabular data lacking spatial relationships. To address these challenges, we propose a Tensorized Image Generator (TIG) algorithm that converts longitudinal tabular data into structured image representations. TIG systematically maps features to specific pixel positions while preserving the spatial proximity of related features, enabling the use of CNNs for effective spatial feature extraction. We utilize longitudinal neuropsychiatric data from the FTLDNI archive and develop a hybrid deep learning framework integrating CNNs with Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), to model temporal dynamics. This approach forecasts FTD progression up to four years into the future. Benchmarking results reveal that CNN-LSTM and CNN-GRU models trained on TIG-generated images achieve significantly lower mean squared and mean absolute errors compared to models trained on original tabular data and images from existing conversion methods (e.g., IGTD). Additionally, TIG-generated images outperform spatio-temporal ConvLSTM2D models by preserving feature neighborhood structure more effectively, offering a novel and efficient solution for FTD progression forecasting.