在头部CT扫描中检测关键发现的深度学习算法:一个回顾性研究
Sasank Chilamkurthy1, Rohit Ghosh1, Swetha Tanamala1
1Qure.ai, Goregaon East, Mumbai, India.
Lancet (London, England)
|October 16, 2018
概括
深度学习算法准确地检测到头部CT扫描中的关键发现, 这项技术有望实现紧急病例识别的自动化, 并改善紧急情况下的患者分组.
科学领域:
- 放射学
- 人工智能
- 医学成像
背景情况:
- 没有对比的头部CT扫描是头部创伤和中风评估的标准.
- 需要自动检测关键异常以提高效率.
研究的目的:
- 开发和验证深度学习算法,用于自动检测内出血,骨骨折,中线转移和头部CT扫描的质量效应.
主要方法:
- 从印度中心回顾收集313,318个头部CT扫描 (2011-2017).
- 使用Qure25k和CQ500数据集进行开发和验证.
- 使用接收器运行特征曲线下的面积 (AUC) 评估性能.
主要成果:
- 在检测内出血时达到高AUC (0. 92在Qure25k,0. 94在CQ500).
- 精确检测内出血,骨骨折 (AUC 0. 92),中线偏移 (AUC 0. 93) 和质量效应 (AUC 0. 86).
结论:
- 深度学习算法在识别紧急头部CT异常方面具有很高的准确性.
- 有可能对头部创伤或中风症状的患者进行自动分类.
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