Background Story recall is a simple and sensitive cognitive test that is commonly used to measure changes in episodic memory function in early Alzheimer disease (AD). Recent advances in digital technology and natural language processing methods make this test a candidate for automated administration and scoring. Multiple parallel test stimuli are required for higher-frequency disease monitoring. Objective This study aims to develop and validate a remote and fully automated story recall task, suitable for longitudinal assessment, in a population of older adults with and without mild cognitive impairment (MCI) or mild AD. Methods The “Amyloid Prediction in Early Stage Alzheimer’s disease” (AMYPRED) studies recruited participants in the United Kingdom (AMYPRED-UK: NCT04828122) and the United States (AMYPRED-US: NCT04928976). Participants were asked to complete optional daily self-administered assessments remotely on their smart devices over 7 to 8 days. Assessments included immediate and delayed recall of 3 stories from the Automatic Story Recall Task (ASRT), a test with multiple parallel stimuli (18 short stories and 18 long stories) balanced for key linguistic and discourse metrics. Verbal responses were recorded and securely transferred from participants’ personal devices and automatically transcribed and scored using text similarity metrics between the source text and retelling to derive a generalized match score. Group differences in adherence and task performance were examined using logistic and linear mixed models, respectively. Correlational analysis examined parallel-forms reliability of ASRTs and convergent validity with cognitive tests (Logical Memory Test and Preclinical Alzheimer’s Cognitive Composite with semantic processing). Acceptability and usability data were obtained using a remotely administered questionnaire. Results Of the 200 participants recruited in the AMYPRED studies, 151 (75.5%)—78 cognitively unimpaired (CU) and 73 MCI or mild AD—engaged in optional remote assessments. Adherence to daily assessment was moderate and did not decline over time but was higher in CU participants (ASRTs were completed each day by 73/106, 68.9% participants with MCI or mild AD and 78/94, 83% CU participants). Participants reported favorable task usability: infrequent technical problems, easy use of the app, and a broad interest in the tasks. Task performance improved modestly across the week and was better for immediate recall. The generalized match scores were lower in participants with MCI or mild AD (Cohen d=1.54). Parallel-forms reliability of ASRT stories was moderate to strong for immediate recall (mean rho 0.73, range 0.56-0.88) and delayed recall (mean rho=0.73, range=0.54-0.86). The ASRTs showed moderate convergent validity with established cognitive tests. Conclusions The unsupervised, self-administered ASRT task is sensitive to cognitive impairments in MCI and mild AD. The task showed good usability, high parallel-forms reliability, and high convergent validity with established cognitive tests. Remote, low-cost, low-burden, and automatically scored speech assessments could support diagnostic screening, health care, and treatment monitoring.

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Introduction Artificial intelligence (AI) systems leveraging speech and language changes could support timely detection of Alzheimer's disease (AD). Methods The AMYPRED study (NCT04828122) recruited 133 subjects with an established amyloid beta (Aβ) biomarker (66 Aβ+, 67 Aβ–) and clinical status (71 cognitively unimpaired [CU], 62 mild cognitive impairment [MCI] or mild AD). Daily story recall tasks were administered via smartphones and analyzed with an AI system to predict MCI/mild AD and Aβ positivity. Results Eighty‐six percent of participants (115/133) completed remote assessments. The AI system predicted MCI/mild AD (area under the curve [AUC] = 0.85, ±0.07) but not Aβ (AUC = 0.62 ±0.11) in the full sample, and predicted Aβ in clinical subsamples (MCI/mild AD: AUC = 0.78 ±0.14; CU: AUC = 0.74 ±0.13) on short story variants (immediate recall). Long stories and delayed retellings delivered broadly similar results. Discussion Speech‐based testing offers simple and accessible screening for early‐stage AD.

INTRODUCTION: Longitudinal data is key to identifying cognitive decline and treatment response in Alzheimer's disease (AD). METHODS: The Automatic Story Recall Task (ASRT) is a novel, fully automated test that can be self-administered remotely. In this longitudinal case-control observational study, 151 participants (mean age: 69.99 (range 54-82), 73 mild cognitive impairment/mild AD and 78 cognitively unimpaired) completed parallel ASRT assessments on their smart devices over 7-8 days. Responses were automatically transcribed and scored using text similarity metrics. RESULTS: Participants reported good task usability. Adherence to optional daily assessment was moderate. Parallel forms correlation coefficients between ASRTs were moderate-high. ASRTs correlated moderately with established tests of episodic memory and global cognitive function. Poorer performance was observed in participants with MCI/Mild AD. DISCUSSION: Unsupervised ASRT assessment is feasible in older and cognitively impaired people. This automated task shows good parallel forms reliability and convergent validity with established cognitive tests.

BackgroundChanges in speech, language, and episodic and semantic memory are documented in Alzheimer’s disease (AD) years before routine diagnosis.AimsDevelop an Artificial Intelligence (AI) system detecting amyloid-confirmed prodromal and preclinical AD from speech collected remotely via participants’ smartphones.MethodA 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 Aβ status and MCI/mild AD within the full sample and Aβ status in clinical diagnostic subsamples.FindingsAt 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.InterpretationCombined with advanced AI language models, brief, remote speech-based testing offers simple, accessible and cost-effective screening for early stage AD.FundingNovoic.Research in contextEvidence before this studyRecent 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 studyThis 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 evidencePrior 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.