基于科尔摩戈罗夫-阿诺尔德网络,对暴露于水泥灰的患者进行定量计算机断层成像分类
Ngan-Khanh Chau1, Woo Jin Kim2, Chang Hyun Lee3
1School of Mechanical Engineering & IEDT, Kyungpook National University, Daegu, Republic of Korea; An Giang University, Vietnam National University - Ho Chi Minh City, Ho Chi Minh, Viet Nam.
Artificial intelligence in medicine
|June 1, 2025
概括
科尔莫戈罗夫-阿诺德网络 (KANs) 通过使用定量计算断层扫描 (QCT) 成像,准确地检测出暴露于水泥灰的呼吸系统问题. 这种人工智能方法增强了职业健康中肺部疾病的早期诊断和解释.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 职业健康 职业健康 职业健康
背景情况:
- 职业健康评估对于识别与工作场所暴露,如水泥灰尘相关的呼吸系统问题至关重要.
- 定量计算断层扫描 (QCT) 提供了详细的肺洞察力,但由于高维度,对传统的机器学习提出了挑战.
研究的目的:
- 评估Kolmogorov-Arnold网络 (KANs) 的有效性,以对QCT成像数据进行二进制分类.
- 评估暴露于水泥尘埃的个体的呼吸系统状况.
主要方法:
- 使用了609个人的QCT图像 (311个暴露于水泥尘埃,298个对照).
- 导出了141个QCT变量,并应用了两个隐藏层 (15和8个神经元) 的KAN.
- 执行了参数微调,五倍交叉验证,以及用于特征解释的SHAP分析.
主要成果:
- KAN实现了高性能:98.03%的准确性,97.35%的精度,98.70%的回忆,98.01%的F1分数,97.40%的特异性.
- 该模型的性能优于传统方法和其他深度学习方法.
- SHAP分析确定了关键预测因素,包括气道几何,壁厚度和肺体积.
结论:
- KANs显著提高了QCT数据的分类,用于早期检测水泥灰相关的呼吸道疾病.
- SHAP分析提供模型可解释性,支持职业健康的临床翻译.
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