使用彩色活动来识别儿童视觉运动整合的发展:人工智能的应用
Tzu-Yun Huang1,2, Kuan-Lin Chen1,2,3, Gong-Hong Lin4
1Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan City, Taiwan.
Annals of medicine
|November 3, 2025
概括
这项研究开发了一个AI模型,使用着色活动来评估学龄前儿童的视觉运动整合 (VMI). 人工智能模型显示出作为查工具的承诺,用于识别儿童的VMI发育状态.
科学领域:
- 儿科发展评估儿童的发展评估.
- 医疗保健中的人工智能
- 儿童学习障碍 儿童学习障碍
背景情况:
- 视觉运动集成 (VMI) 对儿童的发展和学习至关重要.
- 在VMI的困难往往与学习障碍有关.
- 早期识别VMI状态对于及时干预至关重要.
研究的目的:
- 评估彩色活动在学龄前儿童中评估VMI的潜力.
- 开发和验证人工智能 (AI) 模型,用于预测VMI发展状态.
- 建立一种新的,可访问的VMI查方法.
主要方法:
- 利用了505名学龄前儿童的数据,其中404名用于培训,101名用于测试.
- 使用Beery-Buktenica视觉运动整合发展测试,第四版 (VMI-4) 作为基准.
- 开发了一个人工智能模型,结合了支持向量机 (SVM) 回归和极端梯度增强 (XGBoost) 分类.
主要成果:
- 人工智能模型在培训数据上实现了高准确度 (86.2%),灵敏度 (84.7%) 和特异性 (85.4%).
- 在测试数据上,该模型在准确度 (80.20%),灵敏度 (73.68%) 和特异性 (81.71%) 方面表现良好.
- 人工智能模型有效地根据着色活动表现预测了VMI发育状态.
结论:
- 配色活动与人工智能相结合,为VMI提供了可行的选工具.
- 这种方法为确定儿童VMI发育状态提供了一种有希望的方法.
- 进一步的研究可以探索这种人工智能驱动的选工具的更广泛应用.
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