Differentiating memory clinic patients and healthy volunteers using machine‐learning analysis of speech and eye movements during a reading task
Bibliographic record
Abstract
Abstract Background 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. Method We recruited individuals with a clinical diagnosis of AD, mild cognitive impairment (MCI), and subjective memory complaints (SMC) from a subspecialty memory clinic, and controls from the community. Clinical diagnosis was ascertained by trained Behavioural Neurologists aided by cognitive tests and neuroimaging. Participants read a paragraph from the International Reading Speed Texts (IReST). Speech was recorded, automatically transcribed, and manually verified. Eye movements were recorded with an infrared eye‐tracker. Features extracted included lexical and acoustic parameters for speech, fixation and saccade‐related features from gaze, and novel multimodal features leveraging speech and gaze signals in combination. We explored predictive models combining using logistic regression, Gaussian Naïve Bayes classifiers, and Random Forests. Result Here we report baseline IReST task data from 60 clinic patients (12% SMC, 30% MCI, 58% AD, mean age 73 ± 9, 52% female) and 66 controls (mean age 65 ± 9, 68% female). Best speech‐based models distinguished patients from controls with an Area Under the ROC Curve (AUC) of 0.75 (95% CI 0.72‐0.78). Best gaze models yielded an AUC of 0.73 (0.71‐0.75). Models integrating speech and gaze data yielded best results with AUC 0.78 (0.76‐0.80). We have previously reported data from a separate task where participants describe the Cookie Theft photo from the Boston Aphasia Battery while undergoing infrared eye‐tracking; the best AUC model combining speech and gaze was 0.80 (0.78 ‐ 0.92). Conclusion Machine‐learning based analysis of speech and gaze data demonstrates promising classification accuracy in distinguishing memory clinic patients from healthy controls, particularly when leveraging speech and gaze data in combination. We will explore the additional classification accuracy of combining data from multiple tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".