A IA consegue prever surtos de esclerose múltipla a partir de alterações nos padrões de velocidade de digitação no smartphone ?

A esclerose múltipla interrompe os sinais nervosos, afetando subtilmente o controlo motor fino. A IA, ao analisar a dinâmica da digitação (velocidade, ritmo, erros), poderá detetar o agravamento da inflamação antes de surgirem sinais clínicos. Dados longitudinais provenientes do uso quotidiano do telemóvel poderiam assinalar recaídas sem necessidade de visitas à clínica. Preocupações com a privacidade e a variabilidade do comportamento do utilizador complicam a validação. A abordagem combina deteção passiva com análise preditiva.

Background

Multiple sclerosis disrupts nerve signals, subtly affecting fine motor control. AI analyzing typing dynamics (speed, rhythm, errors) might detect worsening inflammation before clinical signs appear. Longitudinal data from everyday phone use could flag relapses without clinic visits. Privacy concerns and user behavior variability complicate validation. The approach merges passive sensing with predictive analytics. AI can already extract keystroke-timing features from smartphone sensors and detect changes in typing cadence at clinically meaningful levels, but translating those signals into reliable multiple sclerosis (MS) flare-up forecasts remains experimental. Small-scale studies (N≈80–200 relapsing-remitting MS patients) have shown that typing-speed variability rises days to weeks before symptom exacerbation, yielding modest predictive performance (AUC≈0.72–0.78) when combined with passive activity and sleep data. The main bottleneck is generalisability across diverse keyboards, languages and patient cohorts, plus ethical and regulatory hurdles for medical-grade apps. Larger, prospective trials with continuous, real-world typing capture are now underway to validate clinical utility.