AI and speech-based identification of cognitive impairment in maintenance haemodialysis-the ASPIRE study.

Background: Cognitive impairment (CI) is common among individuals receiving maintenance haemodialysis and is associated with impaired treatment adherence, reduced quality of life, and mortality. However, CI often remains under-recognized in nephrology care because existing diagnostic approaches are resource-intensive. Speech-based artificial intelligence (AI) is a promising approach for identifying CI through analysis of speech characteristics. This proof-of-concept study investigated whether a speech-based AI model could identify Montreal Cognitive Assessment (MoCA)-based CI and distinguish individuals receiving haemodialysis from clinically relevant controls. Methods: Adults receiving maintenance haemodialysis and controls completed the MoCA and speech tasks. Participants were categorized into low-, moderate-, and high-risk CI groups according to MoCA scores. Acoustic, linguistic, and multimodal speech features were extracted using machine-learning models. Model performance was evaluated. Results: Speech-based AI differentiated individuals receiving haemodialysis from controls ( Conclusion: Speech-based AI suggested potential as a scalable, non-invasive, and low-burden approach for identifying CI. Verbal fluency tasks and multimodal speech analysis showed the strongest classification performance. Although larger multicentre studies are needed, these findings provide proof of concept that speech may serve as a clinically relevant biomarker of CI in nephrology and support further validation using comprehensive neuropsychological assessment as a reference standard.