可解释和干预的基于超声波的机器学习模型用于儿科尾炎
Ričards Marcinkevičs1, Patricia Reis Wolfertstetter2, Ugne Klimiene1
1Department of Computer Science, ETH Zurich, Universitätstrasse 6, Zürich, 8092, Switzerland.
Medical image analysis
|November 24, 2023
概括
这项研究引入了可解释的机器学习模型,使用超声波图像来预测儿童尾炎. 这些模型为临床医生提供了可以理解的见解,而不会牺牲诊断准确度.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 儿科手术 儿科手术
背景情况:
- 尾炎是小儿腹部手术的常见原因.
- 现有的决策支持系统往往忽略了腹部超声波数据.
- 超声波是非侵入性的,并广泛适用于儿科患者.
研究的目的:
- 开发可解释的机器学习模型,用于尾炎诊断,管理和严重程度预测.
- 为了利用腹部超声波图像,这些图像在当前的系统中经常被充分利用.
- 为临床医生创建可理解和互动的模型.
主要方法:
- 利用概念瓶模型 (CBM) 进行可解释的预测.
- 扩展CBM来处理多视图成像和不完整的概念集.
- 在579名儿科患者的数据集上训练了模型,其中包括1709张超声波图像和临床数据.
主要成果:
- 扩展的多视图CBM实现了诊断预测的AUROC为0.80和AUPR为0.92.
- 这些模型提供了人类可以理解和干预的预测.
- 性能与相同数据集上的黑子神经网络相比.
结论:
- 使用超声波的可解释机器学习模型可以有效地预测儿童的尾炎.
- 拟议的CBM方法为尾炎评估提供了一个有价值的,临床医生友好的工具.
- 这种方法通过整合易于获得的超声波数据来增强决策支持.
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