Evaluation of a speech-based AI system for early detection of Alzheimer’s disease remotely via smartphones
Authors: Fristed Emil , Skirrow Caroline , Meszaros Marton , Lenain Raphael , Meepegama Udeepa , Cappa Stefano , Aarsland Dag , Weston Jack 
Published: October 2021

Background

Changes in speech, language, and episodic and semantic memory are documented in Alzheimer’s disease (AD) years before routine diagnosis.

Aims

Develop an Artificial Intelligence (AI) system detecting amyloid-confirmed prodromal and preclinical AD from speech collected remotely via participants’ smartphones.

Method

A convenience sample of 133 participants with established amyloid beta and clinical diagnostic status (66 Aβ+, 67 Aβ-; 71 cognitively unimpaired (CU), 62 with mild cognitive impairment (MCI) or mild AD) completed clinical assessments for the AMYPRED study (NCT04828122). Participants completed optional remote assessments daily for 7-8 days, including the Automatic Story Recall Task (ASRT), a story recall paradigm with short and long variants, and immediate and delayed recall phases. Vector-based representations from each story source and transcribed retelling were produced using ParaBLEU, a paraphrase evaluation model. Representations were fed into logistic regression models trained with tournament leave-pair-out cross-validation analysis, predicting Aβstatus and MCI/mild AD within the full sample and Aβstatus in clinical diagnostic subsamples.

Findings

At least one full remote ASRT assessment was completed by 115 participants (mean age=69.6 (range 54-80); 63 female/52 male; 66 CU and 49 MCI/mild AD, 56 Aβ+ and 59 Aβ-). Using an average of 2.7 minutes of automatically transcribed speech from immediate recall of short stories, the AI system predicted MCI/mild AD in the full sample (AUC=0.85 +/- 0.08), and amyloid in MCI/mild AD (AUC=0.73 +/- 0.14) and CU subsamples (AUC=0.71 +/- 0.13). Amyloid classification within the full sample was no better than chance (AUC=0.57 +/- 0.11). Broadly similar results were reported for manually transcribed data, long ASRTs and delayed recall.

Interpretation

Combined with advanced AI language models, brief, remote speech-based testing offers simple, accessible and cost-effective screening for early stage AD.

Funding

Novoic.

Research in context

Evidence before this study

Recent systematic reviews have examined the use of speech data to detect vocal and linguistic changes taking place in Alzheimer’s dementia. Most of this research has been completed in the DementiaBank cohort, where subjects are usually in the (more progressed) dementia stages and without biomarker confirmation of Alzheimer’s disease (AD). Whether speech assessment can be used in a biomarker-confirmed, early stage (preclinical and prodromal) AD population has not yet been tested. Most prior work has relied on extracting manually defined “features”, e.g. the noun rate, which has too low a predictive value to offer clinical utility in an early stage AD population. In recent years, audio- and text-based machine learning models have improved significantly and a few studies have used such models in the context of classifying AD dementia. These approaches could offer greater sensitivity but it remains to be seen how well they work in a biomarker-confirmed, early stage AD population. Most studies have relied on controlled research settings and on manually transcribing speech before analysis, both of which limit broader applicability and use in clinical practice.

Added value of this study

This study tests the feasibility of advanced speech analysis for clinical testing of early stage AD. We present the results from a cross-sectional sample in the UK examining the predictive ability of fully automated speech-based testing in biomarker-confirmed early stage Alzheimer’s disease. We use a novel artificial intelligence (AI) system, which delivers sensitive indicators of AD-at-risk or subtle cognitive impairment. The AI system differentiates amyloid beta positive and amyloid beta negative subjects, and subjects with mild cognitive impairment (MCI) or mild AD from cognitively healthy subjects. Importantly the system is fully remote and self-contained: participants’ own devices are used for test administration and speech capture. Transcription and analyses are automated, with limited signal loss. Overall the results support the real-world applicability of speech-based assessment to detect early stage Alzheimer’s disease. While a number of medical devices have recently been approved using image-based AI algorithms, the present research is the first to demonstrate the use case and promise of speech-based AI systems for clinical practice.

Implications of all the available evidence

Prior research has shown compelling evidence of speech- and language-based changes occurring in more progressed stages of Alzheimer’s disease. Our study builds on this early work to show the clinical utility and feasibility of speech-based AI systems for the detection of Alzheimer’s disease in its earliest stages. Our work, using advanced AI systems, shows sensitivity to a biomarker-confirmed early stage AD population. Speech data can be collected with self-administered assessments completed in a real world setting, and analysed automatically. With the first treatment for AD entering the market, there is an urgent need for scalable, affordable, convenient and accessible testing to screen at-risk subject candidates for biomarker assessment and early cognitive impairment. Sensitive speech-based biomarkers may help to fulfil this unmet need.