In Alzheimer's & dementia (Amsterdam, Netherlands)
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.
Fristed Emil, Skirrow Caroline, Meszaros Marton, Lenain Raphael, Meepegama Udeepa, Cappa Stefano, Aarsland Dag, Weston Jack
2022
“Alzheimers disease”, artificial intelligence, clinical assessment, clinical screening, deep learning, diagnostics, digital health, episodic memory, language, machine learning, mild cognitive impairment, remote, speech
