在人工智能世界中为忙碌的临床成像专业人员:在没有数学的情况下获得关于深度学习的直觉
Dolly Y Wu1, Dat T Vo2, Stephen J Seiler3
1Volunteer Services, UT Southwestern Medical Center, Dallas, TX, USA.
Journal of medical imaging and radiation sciences
|October 22, 2024
概括
本研究引入了实践模拟,以消除医学诊断中的深度学习 (DL) 的神秘性. 了解DL算法有助于临床医生解释计算机辅助诊断结果并改善患者护理.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 医学诊断在很大程度上依赖于图像和症状中的模式识别.
- 深度学习算法 (DLs) 在这些任务中表现出色,但通常充当"黑子".
- 临床专业人员需要了解DLS来解释计算机辅助诊断 (CAD) 结果并指导未来的开发.
研究的目的:
- 为临床专业人员提供对医疗DLS的基本理解.
- 为了阐明用于诊断的DL算法的内部运作.
- 加强有关CAD系统的决策和患者沟通.
主要方法:
- 为医疗DL开发了易于实施的演示和模拟练习.
- 利用相关的医学类比,如乳腺癌诊断和分期.
- 专注于通过交互式模拟观察深度学习的实践.
主要成果:
- 模拟显示了DL准确性和数据数量/质量之间的复杂关系.
- 证明了数据特征如何影响诊断预测的可靠性.
- 提供了DL的临床应用的实际经验教训和影响.
结论:
- 用模拟进行实践学习可以为临床医生解开DL的神秘性.
- 更深入地了解DL有助于解释诊断结果并提高患者的信心.
- 讨论的原则适用于诊断和治疗相关的DL应用.
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