基于机器学习的区域和全球脊柱参数测量,使用曲折点的冲击角概念
Thong Phi Nguyen1,2, Ji-Hwan Kim3, Seong-Ha Kim3
1Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea.
Bioengineering (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了曲点 (IAIP) 和卷积神经网络 (CNN) 的发生角度,用于分析脊柱斜对齐. 该CNN模型实现了80%的脊柱角度检测率,改善了脊柱健康数据分析.
科学领域:
- 整形外科和脊椎外科手术
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 脊柱斜腰对齐对于脊柱健康和生物力学至关重要.
- 评估脊柱对齐的传统方法可能是主观的,耗时的.
- 开发客观和高效的分析工具对于临床实践至关重要.
研究的目的:
- 评估卷积神经网络 (CNN) 的应用,以评估脊柱斜腰对齐.
- 介绍和评估曲折点 (IAIPs) 发生角的实用性,以捕捉骨盆和脊柱的相互作用.
- 将IAIP与机器学习进行集成,以进行先进的斜对齐分析.
主要方法:
- 使用来自大量患者队列的全脊柱侧面放射图.
- 开发并应用基于CNN的机器学习模型进行参数评估.
- 引入并测量了与传统脊柱参数一起的新型IAIP.
主要成果:
- 在CNN模型中,脊柱角的检测率达到了80% (例如,腰部位,胸部位),误差值为3.5°.
- 盆腔和C2发生角度显示出强大的成功率.
- 来自新型公式的测量与CNN衍生的测量相对相关.
结论:
- 基于CNN的深度学习可以精确测量脊柱斜参数.
- 结合机器学习的IAIP概念,提供了一个有前途的方法,用于全面的斜腰脊柱对齐分析.
- 这种方法提升了脊柱健康评估的理解和数据积累.
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