Papers by Sally Newton-Mason
Differentiating memory clinic patients and healthy volunteers using machine‐learning analysis of speech and eye movements during a reading task
Alzheimer's & Dementia, 2021
Clinical trials investigating novel disease‐modifying therapies for Alzheimer’s disease (AD) are ... more Clinical trials investigating novel disease‐modifying therapies for Alzheimer’s disease (AD) are increasingly targeting participants with preclinical or early‐stage neurodegeneration. Artificial intelligence may improve ascertainment of these individuals and thus enrich clinical trial cohorts. We examined classification accuracy of machine learning analysis of speech and gaze data to distinguish memory clinic patients from controls.
Alzheimer's & Dementia, 2020
Clinical trials of disease‐modifying therapies for Alzheimer’s disease (AD) are increasingly focu... more Clinical trials of disease‐modifying therapies for Alzheimer’s disease (AD) are increasingly focused on recruiting individuals with preclinical or early‐stage disease. Artificial intelligence may help in enriching clinical trial populations with high‐risk individuals. We analyzed prospectively‐collected speech and eye‐tracking data to distinguish individuals with mild‐moderate AD, mild cognitive impairment (MCI), and subjective memory complaints (SMC) from age and sex‐matched healthy volunteers.
Alzheimer’s disease (AD) is an insidious progressive neurodegenerative disease resulting in impai... more Alzheimer’s disease (AD) is an insidious progressive neurodegenerative disease resulting in impaired cognition, dementia, and eventual death. At the earliest stages of the disease, decline in multiple cognitive domains including speech and eye movements occurs, and worsens with disease progression. Therefore, investigating speech and eye movements is promising as a non-invasive method for early classification of AD. While related work has investigated AD classification using speech collected during spontaneous speech tasks, no prior research has studied the utility of eye movements and their combination with speech for this classification task. In this paper, we present classification experiments with speech and eye movement data collected from 68 memory clinic patients (with a diagnosis of AD, mixed dementia, mild cognitive impairment, or subjective memory complaints) and © 2020 O. Barral*, H. Jang*, S. Newton-Mason, S. Shajan, T. Soroski, G. Carenini, C. Conati & T. Field. Non-Inv...
Evaluating Web-Based Automatic Transcription for Alzheimer’s Speech Data: Transcript Comparison and Machine Learning Analysis (Preprint)
BACKGROUND Speech data for medical research can be collected non-invasively and in large volumes.... more BACKGROUND Speech data for medical research can be collected non-invasively and in large volumes. Speech analysis has shown promise in diagnosing neurodegenerative disease. To effectively leverage speech data, transcription is important as there is valuable information contained in lexical content. Manual transcription, while highly accurate, limits potential scalability and cost savings associated with language-based screening. OBJECTIVE To better understand the use of automatic transcription for classification of neurodegenerative disease (Alzheimer’s Disease [AD], mild cognitive impairment [MCI] or subjective memory complaints [SMC] versus healthy controls), we compared automatically generated transcripts against transcripts that went through manual correction. METHODS We recruited individuals from a memory clinic (“patients”) with a diagnosis of mild-moderate AD, (n=44), MCI (n=20), SMC (n=8) and healthy controls living in the community (n=77). Participants were asked to describ...
Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data
Frontiers in Human Neuroscience
Alzheimer’s disease (AD) is a progressive neurodegenerative condition that results in impaired pe... more Alzheimer’s disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification. The cohort includes data from two novel tasks not previously assessed in classification models for AD (pupil fixation and description of a pleasant past experience), as well as two established tasks (picture description and paragraph reading). Our dataset includes language and eye movement data from 79 memory clinic patients with diagnoses of mild-moderate AD, mild cognitive impairment (MCI), or subjective memory complaints (SMC), and 83 older adult controls. The analysis of the individual novel tasks showed similar classification accuracy when compared to established tasks, demonstrating their discrimin...
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Papers by Sally Newton-Mason