使用手部X射线进行骨年龄评估,以确定生长问题
Muhammad Umer1, Ala' Abdulmajid Eshmawi2, Khaled Alnowaiser3
1Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
PeerJ. Computer science
|December 11, 2023
概括
使用X射线图像准确估计骨年龄对于诊断儿科生长障碍至关重要. 一个新的定制卷积神经网络 (CNN) 在评估骨年龄方面取得了97%的准确性,超过了现有的模型.
科学领域:
- 儿科放射学 儿科放射学
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
背景情况:
- 从X射线来估计骨年龄对于识别婴儿和新生儿骨生长异常至关重要.
- 骨异常可能源于各种疾病,影响生长板,并可能导致永久性关节损伤.
- 时间和骨年龄之间的差异表明潜在的生长问题,需要对早期诊断进行准确的评估.
研究的目的:
- 开发一种自动化系统,使用儿科手部X射线精确估计骨年龄.
- 为了评估定制卷积神经网络 (CNN) 检测手骨成熟的有效性.
- 将拟议的CNN模型与视觉几何组 (VGG) 模型的性能进行比较.
主要方法:
- 利用北美放射学会的儿科骨年龄挑战数据集 (12,600张图像).
- 开发了一个定制的卷积神经网络 (CNN) 模型用于骨年龄评估.
- 采用数据增强技术来增强数据集大小和模型培训.
主要成果:
- 定制的CNN模型在骨年龄估计中实现了97%的准确性.
- 与VGG模型相比,拟议的CNN模型表现出优越的性能.
- 数据增强技术对模型的训练和准确性产生了积极的影响.
结论:
- 开发的定制CNN模型为骨年龄估计提供了高度准确和高效的方法.
- 这种人工智能驱动的方法可以帮助临床医生诊断儿童的生长异常和内分泌疾病.
- 这些发现突显了深度学习在推进儿科放射学评估方面的潜力.
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