一种新的视觉变压器模型产生了与专家人类编码器一样准确的时钟绘图测试成绩
Mengyao Hu1,2, Tian Qin3, Richard Gonzalez4
1The University of Texas Health Science Center at Houston, Houston, USA. Mengyao.Hu@uth.tmc.edu.
Scientific reports
|January 13, 2026
概括
这项研究开发了一个使用视觉转换器的人工智能系统,自动对痴呆症查的时钟图纸进行评分,实现专家级准确性. 这种智能时钟评分系统通过减少手动编码偏差来增强大规模的痴呆症研究.
科学领域:
- 人工智能的人工智能
- 神经科学是一个神经科学.
- 医疗成像医学成像
背景情况:
- 阿尔茨海默病和相关的痴呆症构成了重大的公共卫生挑战.
- 时钟绘图测试是一种常见的痴呆症查工具,但需要手动编码,引入潜在的偏差.
- 自动化时钟绘图分析对于大规模的痴呆症研究至关重要.
研究的目的:
- 开发和评估一个由人工智能驱动的系统,用于自动评分时钟绘图图像.
- 为了比较不同深度学习神经网络 (DLNN) 时钟计分方法的性能.
- 引入和评估一个顺序分数系统,考虑分数层次结构.
主要方法:
- 利用了来自国家健康和衰老趋势研究 (NHATS) 的大量时钟图像数据集.
- 我们比较了三个DLNN模型:ResNet101,EfficientNet和视觉变压器.
- 实施和评估二进制和顺序 (0-5) 评分系统,与专家手动编码进行比较.
主要成果:
- 视觉转换器实现了最高的准确性,与专家人类编码器相提并论 (加权卡帕 = 0.81).
- ResNet101和EfficientNet的性能在加权kappa = 0.56-0.73.3之间.
- 顺序编码系统证明了在痴呆症评估中最大限度地减少估计错误的潜力.
结论:
- 基于人工智能的时钟评分系统,特别是使用视觉转换器,可以准确有效地自动化痴呆症查.
- 这种自动化方法减轻了手动编码偏差,并提高了大规模痴呆症研究的可靠性.
- 自2022年以来,基于视觉变压器的系统已集成到NHATS中,证明了其实际效用.
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