在现实世界的临床环境中,用于早期痴呆症预测的强大和可解释的人工智能引导标记
Liz Yuanxi Lee1, Delshad Vaghari1, Michael C Burkhart1
1Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom.
EClinicalMedicine
|January 7, 2025
概括
一个新的AI模型使用认知测试和MRI扫描准确预测阿尔茨海默病的进展. 这种可解释的工具为早期痴呆症检测提供了更精确的标记,有助于临床管理和减少误诊.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 早期痴呆症预测对于患者的结果至关重要,但缺乏敏感的分层工具.
- 目前用于痴呆症预测的机器学习模型的解释性和概括性有限,阻碍了临床采用.
- 准确的早期诊断和预后对于防止误诊和改善患者管理至关重要.
研究的目的:
- 开发一个强大,可解释和可通用的预测预后模型 (PPM) 用于早期痴呆症检测.
- 用现实世界,多式联络患者数据验证PPM的临床实用性.
- 创建一个个性化的人工智能引导的标记物,用于预测阿尔茨海默病 (AD) 的进展.
主要方法:
- 训练了一种预测性预后模型 (PPM),使用临床相关的预测因素,如认知测试和灰质缩.
- 在英国和新加坡的记忆诊所的独立,多中心,现实世界的数据上验证了PPM的预测.
- 利用非侵入性和低成本的患者数据,包括认知评估和结构性MRI扫描.
主要成果:
- PPM证明了从轻度认知障碍 (MCI) 到阿尔茨海默病 (AD) 的进展的强有力的预测,准确率为81.66%,AUC为0.84,灵敏度为82.38%,特异性为80.94%.
- 该模型在研究和来自多个记忆诊所的真实世界患者数据中进行了概括.
- 由人工智能引导的衍生个性化标志物比标准临床标志物 (HR=3.42,p=0.01) 和临床诊断 (HR=2.84,p<0.01) 更准确地预测了AD进展.
结论:
- 开发的PPM是一种强大且可解释的人工智能引导的早期痴呆症预测标记.
- 该模型与跨国纵向,多中心数据的验证支持其临床采用潜力.
- 这种人工智能引导的标志物在减少误诊和改善早期痴呆症患者分层方面显著有前途.
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