对阿尔茨海默氏症疾病的视觉分类标准的细分指导开发
Mirjam Peters1, David Steinbart2, Alexander Hammers3
1Department of Medical Radiation Sciences, University of Gothenburg, 41345 Gothenburg, Sweden.
medRxiv : the preprint server for health sciences
|November 19, 2025
概括
自动脑MRI细分可以指导非专家诊断阿尔茨海默病 (AD). 一位新手开发了皮质皮质的分类规则,在识别类似AD的变化方面取得了中等的准确性.
科学领域:
- 神经成像是一种神经成像.
- 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:
- 阿尔茨海默氏症 (AD) 在症状出现多年前就会引起大脑变化,因此通过MRI进行早期检测具有挑战性.
- 仅视觉MRI解释对早期AD检测是不够的.
- 自动细分提供了精确的测量,但在临床评估中未得到充分利用.
研究的目的:
- 调查自动语义细分输出是否可以指导非专家在创建AD的诊断标准.
- 为新手调查人员开发和测试基于细分的工作流程.
主要方法:
- 一位初学者在阿尔茨海默病神经成像计划 (ADNI) 的MRI数据上使用了MAPER细分模型.
- 根据视觉评估和皮质皮质 (PC) 的体积读数,制定了分类规则.
- 根据开发的标准,对独立的ADNI数据进行了盲目评估.
主要成果:
- 基于PC体积 (值<430mm3),形状和全球缩的分类规则在四个诊断组中实现了71%的准确性.
- 当排除轻度认知障碍 (MCI) 病例时,精度提高到77%,重点关注CN和AD.
- 分段导向的视觉工作流程使得非专家诊断中适度的准确性.
结论:
- 自动化细分可以使非专家能够应用对阿尔茨海默病的解剖基础诊断标准.
- 这个框架支持可解释的模型,可解释的AI,并加速诊断技能的获得.
- 这种方法有望在其他大脑区域和诊断任务中得到更广泛的应用.
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