Transforming dementia diagnosis & prognosis through AI
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Abstract
Dementia, characterized by a progressive decline in cognitive function, poses significant societal challenges. This dissertation develops a suite of Artificial Intelligence (AI) approaches that analyze digital data from subjects to assess and predict cognitive decline associated with Alzheimer’s disease and related dementias.First, a deep convolutional neural network (CNN) was applied to images derived from the Clock Drawing Test (CDT), using transfer learning to extract high-level features. Combined with demographic information such as age, the model demonstrated strong performance, yielding high accuracy and sensitivity in identifying cognitive impairment.
Next, language models were employed to automate the diagnosis of cognitive impairment from Electronic Health Records (EHR). Clinical notes were analyzed with the Universal Sentence Encoder, focusing on sentences containing keywords related to dementia and daily activities. The analysis involved multiple binary classification tasks, where the language model generated text features from random and encounter-based sampling methods, achieving high performance in detecting both dementia and mild cognitive impairment (MCI).
A novel natural language processing (NLP) approach was also developed to identify different stages of dementia using automated transcriptions of voice recordings from neuropsychological (NP) tests. The pipeline incorporated speech recognition, speech diarization, transformer-based sentence encoding, and logistic regression models. This method achieved strong predictive performance for distinguishing between different cognitive stages, from normal cognition to dementia and MCI. Building on the voice data, this approach was extended to predict the progression from MCI to Alzheimer’s disease, leveraging speech features alongside demographic factors such as age and education. The results demonstrated promising accuracy and sensitivity, outperforming models using traditional NP test scores.
Finally, smartphone-based cognitive assessments were evaluated for their utility in early- stage detection of cognitive impairment. The approach focuses on text features derived from voice recordings of cognitive tests and embedding them using language models. The proposed method, which combines text features with age data, demonstrates high predictive accuracy, particularly in higher-risk populations.
Through the integration of digital technologies, smartphone-based tests, machine learning, NLP, and computer vision, this research offers accessible and reliable tools for dementia diagnosis and prognosis, with potential for broad clinical applications and early interventions.
Description
2025