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

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ID: 328527
2026
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Abstract
Abstract 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-recognised 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 MoCA-based CI and distinguish individuals receiving haemodialysis from clinically relevant controls. Methods Adults receiving maintenance haemodialysis and controls completed the Montreal Cognitive Assessment (MoCA) and speech tasks. Participants were categorised 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 (n = 40). Multimodal models achieved the highest performance, with accuracies ranging from 0.78 to 0.80 for uraemia classification. For MoCA-based cognitive risk classification (n = 63), verbal fluency tasks yielded the strongest results. The best-performing model was the two-task fusion, which identified the high‑risk group with a weighted F1‑score of 73%, ROC AUC of 79%, and MCC of 0.43. Moderate MoCA-based cognitive risk was more difficult to classify. 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.
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Authors Anne Møller Lohmann, Maria Dolmer Knop, Laurine Dargaud, Abhista Partal, Dimitrios Salogiannis, Henrik Birn, Jeanette Finderup
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Journal clinical kidney journal
Year 2026
DOI
10.1093/ckj/sfag316
URL
https://academic.oup.com/ckj/advance-article-pdf/doi/10.1093/ckj/sfag316/71032519/sfag316.pdf
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