预测阿尔茨海默病的深度学习:全面的审查
Isra Malik1, Ahmed Iqbal2, Yeong Hyeon Gu3
1Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 44000, Pakistan.
Diagnostics (Basel, Switzerland)
|June 27, 2024
概括
本综述调查了人工智能和深度学习方法,用于早期发现阿尔茨海默病 (AD). 它强调了当前的技术,挑战,以及在神经疾病诊断中需要可解释的人工智能.
科学领域:
- 神经学 神经学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经系统疾病,导致认知能力下降,记忆力丧失和死亡.
- 早期诊断对于改善患者生存率至关重要,但传统方法具有挑战性.
- 使用人工智能和深度学习的计算机辅助诊断 (CAD) 系统可以快速检测AD.
研究的目的:
- 为了调查各种模式,特征提取,数据集,机器学习技术和AD检测的验证方法.
- 识别当前基于AI的AD检测文献中的挑战.
- 强调AD诊断的深度学习模型中可解释性和可解释性的重要性.
主要方法:
- 来自主要科学存储库的116篇相关研究论文的系统审查.
- 研究的分类基于模式,特征提取,数据集,机器学习算法和验证方法.
- 分析结果以表格形式呈现,以提高清晰度和易于参考.
主要成果:
- 对各种AI和深度学习技术的全面概述,应用于阿尔茨海默病检测.
- 识别常见的数据集,特征提取策略和在现场使用的验证指标.
- 现有研究中的挑战和局限性的总结,包括对模型可解释性的关注.
结论:
- 人工智能和深度学习在快速准确地检测阿尔茨海默病方面显示出重大前景.
- 解决与数据异质性,标准化和模型可解释性相关的挑战对于未来的进步至关重要.
- 需要进一步的研究,以指导开发可靠和可解释的AI工具,用于诊断AD等神经系统疾病的临床应用.
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