Dementia Related Linguistic Anomaly

Dementia-related linguistic anomalies research focuses on identifying and characterizing changes in speech and language patterns associated with various dementia types, primarily to improve early diagnosis and monitor disease progression. Current research utilizes machine learning, particularly deep learning models like convolutional neural networks (CNNs) and transformer-based language models (like GPT), often incorporating multimodal data (e.g., speech, MRI scans, genetic information) for improved accuracy. These efforts aim to develop objective, reliable biomarkers for dementia detection and potentially facilitate earlier interventions and personalized treatment strategies.