一种通过智能手机筛查早期阿尔茨海默病的远程语音人工智能系统。
A remote speech-based AI system to screen for early Alzheimer's disease via smartphones.
作者信息
Fristed Emil, Skirrow Caroline, Meszaros Marton, Lenain Raphael, Meepegama Udeepa, Cappa Stefano, Aarsland Dag, Weston Jack
机构信息
Novoic Ltd London UK.
IUSS Cognitive Neuroscience (ICoN) Center University School for Advanced Studies Pavia Italy.
出版信息
Alzheimers Dement (Amst). 2022 Nov 3;14(1):e12366. doi: 10.1002/dad2.12366. eCollection 2022.
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.
引言
利用语音和语言变化的人工智能（AI）系统可支持对阿尔茨海默病（AD）的及时检测。
方法
AMYPRED研究（NCT04828122）招募了133名具有既定淀粉样蛋白β（Aβ）生物标志物（66名Aβ阳性，67名Aβ阴性）和临床状态（71名认知未受损[CU]，62名轻度认知障碍[MCI]或轻度AD）的受试者。通过智能手机进行每日故事回忆任务，并使用人工智能系统进行分析，以预测MCI/轻度AD和Aβ阳性。
结果
86%的参与者（115/133）完成了远程评估。在整个样本中，人工智能系统预测MCI/轻度AD（曲线下面积[AUC]=0.85，±0.07），但不能预测Aβ（AUC=0.62±0.11），在临床子样本（MCI/轻度AD：AUC=0.78±0.14；CU：AUC=0.74±0.13）中，根据短篇故事变体（即时回忆）预测Aβ。长篇故事和延迟复述的结果大致相似。
讨论
基于语音的测试为早期AD提供了简单且可及的筛查方法。
Zotero 插件