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评估婴儿的骨指数角度:基于深度学习的新方法

Farmanullah Jan¹, Atta Rahman¹, Roaa Busaleh¹

¹Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

Journal of imaging

|November 24, 2023

概括

一个新的深度学习框架使用X射线准确地检测婴儿的部发育性形 (DDH). 这种计算工具有助于专家在客观诊断,提高早期检测和治疗成功率这一关节疾病.

科学领域:

医疗成像医学成像
人工智能的人工智能
儿科整形外科儿科整形外科

背景情况:

部发育性形 (DDH) 是一种常见的婴儿部疾病,需要早期诊断才能有效治疗.
准确的DDH诊断依赖于盆腔X射线扫描的专家解释,如果没有专门的培训,这可能是具有挑战性的.
目前的DDH诊断方法可能缺乏客观性和一致性.

研究的目的:

开发和验证用于在婴儿骨盆X射线中客观检测DDH的计算框架.
使用深度学习方法精确测量骨指数角度,这是DDH的关键指标.
为医疗专家创建一个可访问的工具,以帮助早期和准确诊断DDH.

主要方法:

采用了两阶段的深度学习管道,结合实例细分和关键点检测模型.
该框架分析婴儿盆腔X射线图像,以确定表明DDH的部异常.
该系统量化了acetabular指数角度,提供了客观的诊断指标.

主要成果:

深度学习模型在测量acetabular角度方面表现出高精度,平均像素误差为2.862 ± 2.392.2.
与地面真相注释相比,acetabular角度测量误差在2.402 ± 1.963°的范围内.
开发的模型为DDH诊断提供了一个客观和统一的方法.

结论:

拟议的深度学习框架提供了一种可靠和客观的方法来检测婴儿的DDH.
将其集成到移动应用程序中将提高医疗专家的可访问性,减少诊断负担.
这项技术有可能提高早期DDH检测率,从而改善患者的治疗结果.

关键词:

探测器2 探测器2 面具-RCNNN 在线阅读标签: 标签: RCNN 标签: 美国乙肌体指数 (acetabular index) 是一个指数.深度学习是一种深度学习.部发育性发育不良 (DDH)关键点关键点关键点

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