在低剂量计算机断层扫描图像中检测肋骨骨折的可行性,使用基于大型多中心数据集的模型模型
Liang Jin1,2, E Youjun3, Zheng Ye4
1Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
Heliyon
|May 21, 2024
概括
使用RetinaNet的深度学习模型在低剂量计算机断层扫描 (CT) 中有效检测肋骨骨折. 这种方法表现出强的表现,证明可以帮助放射科医生诊断肋骨骨折.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 低剂量计算机断层扫描 (CT) 越来越多地用于减少辐射暴露.
- 准确的肋骨骨折检测对于患者管理至关重要.
- 评估AI在低剂量CT中用于骨折检测的性能至关重要.
研究的目的:
- 评估使用基于RetinaNet的深度学习模型在低剂量CT图像中检测肋骨骨折的可行性.
- 用这个模型来比较低剂量CT与定期剂量的CT在检测肋骨骨折的有效性.
主要方法:
- 在一个大型的内部数据集 (7300个扫描,50,410个骨折) 上训练了一种RetinaNet深度学习模型.
- 外部验证使用了公开数据集 (MICCAI 2020 RibFrac挑战),包括常用剂量和低剂量CT图像.
- 模型的性能与放射科医生的解释进行了比较.
主要成果:
- 在RetinaNet模型中,低剂量CT的灵敏度为0.859,常规剂量CT的灵敏度为0.886.
- 放射学家的灵敏度在低剂量CT中为0.721%,在常用剂量CT中为0.794.
- 该模型表现出强大的性能,与放射科医生相当或超过,特别是在低剂量成像中.
结论:
- 基于RetinaNet的模型可用于在低剂量CT图像中检测肋骨骨折.
- 这种人工智能方法显示出有潜力帮助放射科医生提高肋骨骨折诊断的准确性.
- 低剂量CT,当用AI分析时,可以成为检测肋骨骨折的可行选择.
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