Evaluation of a speech-based AI system for early detection of Alzheimer's disease remotely via smartphones

Fristed, E.; Skirrow, C.; Meszaros, M.; Lenain, R.; Meepegama, U.; Cappa, S.; Aarsland, D.; Weston, J.

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 A{beta} +, 67 A{beta} -; 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 A{beta} status and MCI/mild AD within the full sample and A{beta} status 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 A{beta} + and 59 A{beta} -). 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.