{Reference Type}: Journal Article
{Title}: NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation.
{Author}: Faria AL;Almeida Y;Branco D;Câmara J;Cameirão M;Ferreira L;Moreira A;Paulino T;Rodrigues P;Spinola M;Vilar M;Bermúdez I Badia S;Simões M;Fermé E;
{Journal}: Front Neurol
{Volume}: 14
{Issue}: 0
{Year}: 2023
{Factor}: 4.086
{DOI}: 10.3389/fneur.2023.1258323
{Abstract}: Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily functioning and independence, with significant societal and economic implications. While neurorehabilitation represents a promising avenue for addressing these deficits, traditional rehabilitation approaches face notable limitations. First, they lack adaptability, offering one-size-fits-all solutions that may not effectively meet each patient's unique needs. Furthermore, the resource-intensive nature of these interventions, often confined to clinical settings, poses barriers to widespread, cost-effective, and sustained implementation, resulting in suboptimal outcomes in terms of intervention adaptability, intensity, and duration. In response to these challenges, this paper introduces NeuroAIreh@b, an innovative cognitive profiling and training methodology that uses an AI-driven framework to optimize neurorehabilitation prescription. NeuroAIreh@b effectively bridges the gap between neuropsychological assessment and computational modeling, thereby affording highly personalized and adaptive neurorehabilitation sessions. This approach also leverages virtual reality-based simulations of daily living activities to enhance ecological validity and efficacy. The feasibility of NeuroAIreh@b has already been demonstrated through a clinical study with stroke patients employing a tablet-based intervention. The NeuroAIreh@b methodology holds the potential for efficacy studies in large randomized controlled trials in the future.