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Updated: Jan 13, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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模拟到现实域适应早期阿尔茨海默病检测从手写动力学使用混合深度学习的手写动力学.

Ikram Bazarbekov¹, Ali Almisreb², Madina Ipalakova¹

¹Department of Computer Engineering, International IT University, Almaty 050040, Kazakhstan.

Sensors (Basel, Switzerland)

|January 10, 2026

概括

本研究介绍了一种使用智能笔的AI框架,通过手写分析来检测早期阿尔茨海默病 (AD). 混合深度学习模型实现了高精度,显示了非侵入性认知评估的潜力.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.模拟到真实的真实人工智能的人工智能是人工智能.深度学习是一种深度学习.数字生物标志物数字生物标志物分析手写的分析.医疗信息学医疗信息学传感器数据传感器数据

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科学领域:

神经科学是一个神经科学.
人工智能的人工智能
数字健康数字健康

背景情况:

阿尔茨海默病 (AD) 导致渐进的认知和运动能力下降,早期检测是一个重大挑战.
传统的诊断方法往往忽略了AD的微妙,临床前迹象.
数字健康和人工智能为识别认知障碍的非侵入性生物标志物提供了新的方法.

研究的目的:

开发和评估一个人工智能驱动的框架,用于早期发现阿尔茨海默病 (AD).
为了利用由传感器集成的智能笔捕获的手写运动数据作为数字生物标志物.
为了比较各种机器学习 (ML) 和深度学习 (DL) 模型对AD检测的性能.

主要方法:

配备了惯性测量单元 (MPU-9250) 的智能笔记录了动态和动态的手写/绘图信号.
评估了ML算法 (逻辑回归,SVM,RF,kNN) 和DL架构 (1D-CNN,LSTM,CNN-BiLSTM) 的使用.
实施了SIM-to-Real域调整策略,以用合成样本来增强有限的数据.

主要成果:

经典的ML模型显示了中等的诊断性能 (AUC:0.62-0.76).
拟议的混合CNN-BiLSTM深度学习模型以0.91准确度和0.96AUC取得了卓越的结果.
该框架使用基于运动的数字生物标志物证明了有效的AD检测.

结论:

来自手写分析的基于运动的数字生物标志物显示了自动化,非侵入性AD检测的巨大潜力.
人工智能框架为数字认知评估提供了一个具有成本效益和可扩展的信息解决方案.
这种方法可以促进早期识别和干预阿尔茨海默病患者.