深度卷积神经网络用于在胸部X射线图上识别肋骨骨折
Shu-Tien Huang1,2,3, Liong-Rung Liu1,2,3, Hung-Wen Chiu3,4
1Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan.
Frontiers in medicine
|August 18, 2023
概括
深度卷积神经网络 (DCNNs) 在胸部X射线图上显示高精度检测肋骨骨折. 这种人工智能方法可以减少错过的诊断,改善创伤患者的治疗结果,降低发病率和死亡率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 肋骨骨折在创伤中很常见,错误诊断增加了患者的发病率和死亡率.
- 目前的成像方法,如CT扫描是昂贵的,涉及辐射;超声波是操作者依赖的.
- 需要准确,可访问和高效的方法来检测肋骨骨折.
研究的目的:
- 评估深层卷积神经网络 (DCNN) 在标准胸部X射线图上识别肋骨骨折的有效性.
- 探索DCNNs作为肋骨骨折现有诊断工具的实际替代方案.
主要方法:
- 编制了记录有肋骨骨折的胸部放射图的回顾性数据集.
- 深度学习模型 (DLM) 在每个类别 (破碎,非破碎,背景) 的2000个兴趣区域 (ROI) 切片上接受了培训.
- 图像被细分为128x128像素,以优化DCNN培训.
主要成果:
- 几种DCNN模型实现了高的验证准确度:AlexNet (92.6%),GoogleLeNet (92.2%),EfficientNetb3 (92.3%),DenseNet201 (92.4%) 和MobileNetV2 (91.2%),这些模型都实现了高的验证准确度.
- 这些结果表明DCNN在区分骨折与非骨折的肋骨方面表现强.
结论:
- 将DCNN整合到临床决策支持系统中可以显著减少错过的肋骨骨折诊断.
- 这种人工智能驱动的方法有望降低创伤患者的发病率和死亡率.
- DCNN为改善胸部创伤诊断和患者护理提供了实用和有效的解决方案.
关键词:
人工智能 (AI) 是一种人工智能.胸部X射线图片 胸部X射线图片深度卷积神经网络 (DCNN) 是一个深度卷积神经网络.深度学习模型 (DLM)肋骨骨折折 肋骨骨折 肋骨骨折 肋骨骨折转移学习转移学习更多相关视频
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