Early cognitive disorders diagnosis is becoming increasingly important due to population aging. The most common causes include Alzheimer's disease and frontotemporal dementia. These diseases are also manifested by changes in speech. NLP allows us to identify and classify these changes. The project aims to develop a web application for self-assessment and automated detection of cognitive disorders from speech. The application will have a form of a dialogue system using machine learning methods. The novelty of this approach is the possibility of an efficient self-assessment of a wide spectrum of the Czech population from their homes and an automated evaluation of test results. Early detection can be followed by a more detailed diagnosis and adequate treatment.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
The difference in results between groups of patients with cognitive impairment and healthy individuals in individual parts of our experimental neuropsychological battery Diagnostic Test: Digitial Diagnostics of Dementia (DDD)
Timeframe: Through study completion, an average of 2 years
Correlation of the results of our experimental battery (DDD) with RBANS
Timeframe: Through study completion, an average of 2 years
Correlation of the results of our experimental battery (DDD) with MAST
Timeframe: Through study completion, an average of 2 years
Correlation of the results of our experimental battery (DDD) with ALBA
Timeframe: Through study completion, an average of 2 years
Correlation of the results of our experimental battery (DDD) with POBAV
Timeframe: Through study completion, an average of 2 years