Predicting dementia progression with fully connected cascade neural networks

Abstract Accurate and timely diagnosis of dementia progression remains a major global challenge due to the complexities of brain pathology and the lack of definitive biomarkers. This study presents a pioneering fully connected cascade (FCC) neural network model that leverages cost-effective lifestyle and neuroimaging data to predict dementia progression with remarkable accuracy. The model uniquely integrates 42 lifestyle factors for brain health (LIBRA) and 7 brain atrophy and lesion indice (BALI) derived from baseline MRI data as inputs, to predict sensitive diffusion tensor imaging (DTI) biomarkers of white matter degeneration. Remarkably, the FCC network achieved a mean squared error of 0.0071693 in predicting DTI metrics, demonstrating exceptional predictive capability. This multidisciplinary data-driven approach capitalizes on the model's ability to detect subtle yet informative changes in brain structure and function through advanced neuroimaging. By amalgamating multidomain lifestyle and neuroimaging data, the proposed model enhances diagnostic value and sensitivity to dementia pathology. Its high accuracy, scalability with large datasets, clinical interpretability, and cost-effectiveness make it a powerful computational tool for early prediction, monitoring, and personalized treatment planning in dementia care. This groundbreaking research exemplifies the transformative potential of artificial intelligence in tackling the global dementia burden, paving the way for improved patient outcomes and reduced healthcare costs.