基于特征的轻度认知障碍和阿尔茨海默氏病的分类基于光学一致性断层血管图像图像
Sarinporn Visitsattapongse1, Areerat Maneerat1, Adisak Trinavarat2
1School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
这项研究引入了一种用于早期发现阿尔茨海默病的新诊断工具,该工具使用视网膜图像上的机器学习. 该方法达到92.17%的准确性,为识别轻度认知障碍和阿尔茨海默病提供了有前途的方法.
科学领域:
- 眼科医生 眼科 眼科
- 神经学 神经学
- 人工智能的人工智能
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,是全球痴呆的主要原因.
- 早期发现AD和轻度认知障碍 (MCI) 对于及时干预和改善患者结果至关重要.
- 目前的诊断方法可能具有侵入性,或缺乏广泛查的可用性.
研究的目的:
- 开发和验证一种新的,非侵入性诊断工具,用于分类MCI和AD.
- 利用机器学习算法应用于用于疾病检测的光学连贯性断层扫描血管学 (OCT-A) 成像.
- 探索新的成像功能,以提高阿尔茨海默病的诊断准确度.
主要方法:
- 从OCT-A图像中提取特征,包括血管密度,叶无血管区面积和视网膜厚度.
- 包括从范围过的OCT-A图像的组图中得出的新特征.
- 基于特征的机器学习的应用,用于使用大型多样化的本地数据库对MCI和AD进行分类.
主要成果:
- 开发的诊断工具在分类MCI和AD方面表现出高准确度.
- 在这项研究中,获得的最佳准确率为92.17%.
- 提取的特征,特别是基于新型组图的特征,证明了疾病分类的有效性.
结论:
- 建议使用OCT-A成像的基于特征的机器学习方法是早期有效诊断阿尔茨海默病的有希望的工具.
- 这种非侵入性方法有可能在早期发现和治疗神经退行性疾病方面发挥重要作用.
- 在更大的多中心研究中进一步验证是有必要的,以确认其临床效用.
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