一个基于人工智能的图像质量控制框架,用于膝盖放射
Hongbiao Sun1, Wenwen Wang1, Fujin He2
1Department of Radiology, Shanghai Changzheng Hospital, Naval Medical University, No.415 Fengyang Road, Huangpu District, Shanghai, 200003, China.
Journal of digital imaging
|June 2, 2023
概括
一个人工智能 (AI) 模型自动化了膝盖放射图质量控制 (QC),与临床医生的准确度相匹配,同时减少了测量时间. 这种AI工具为临床实践中的QC提供了更快,更客观的方法.
科学领域:
- 放射学 放射学是指放射学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 膝盖X光片的手动质量控制 (QC) 是主观的,耗时的.
- 精确的QC对于从X光片中诊断膝盖疾病至关重要.
研究的目的:
- 开发一个自动化的人工智能模型,用于膝盖X射线 QC.
- 与临床医生相比,评估AI模型的性能.
主要方法:
- 开发了一种高分辨率网络 (HR-Net) AI模型,以识别膝盖放射图中的关键点.
- 用几何计算来推导QC标准:AP/LAT重叠比率和LAT曲角度.
- 该模型在大量膝盖放射图数据集上进行了训练和验证,包括外部验证.
主要成果:
- 在AI模型中,在内部和外部验证队列中,AP/LAT骨头重叠和LAT膝盖屈曲角度的高类内一致性系数 (ICC) 与临床医生相比较.
- 在AI模型和临床医生之间,在三个QC标准中没有发现显著差异.
- 与临床医生相比,AI模型显著减少了测量时间.
结论:
- 这种基于人工智能的模型的性能与临床医生在膝盖X射线QC中的表现相似.
- 人工智能模型为临床QC提供了一个更快,潜在的更客观的替代方案.
- 这种人工智能工具显示出提高临床实践效率的巨大潜力.
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