开发一种新的机器学习模型,以使用双平面全身成像来自动化脊柱细分
Yash Lahoti1, Skanda Sai2, Wasil Ahmed1
1Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY.
概括
这项研究开发了一种人工智能算法,用于从双平面全身X射线自动化脊柱细分. 深度学习模型准确地细分脊柱曲率,有助于评估退行性脊椎病变.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 脊柱生物力学 脊柱生物力学
背景情况:
- 退行性脊柱病 (DS) 是一种普遍存在的成年脊柱疾病.
- 双平面全身成像为DS评估提供低剂量,承重的X射线.
- 手动的脊柱曲率量化是劳动密集型的,容易变化.
研究的目的:
- 开发一种人工智能算法,用于自动化脊柱细分.
- 为了使从双平面全身X射线图进行细分,无论脊柱病理或硬件如何.
- 为了标准化和加快在DS患者中脊柱对齐的分析.
主要方法:
- 追溯研究使用250个负载,双平面全身X射线图像 (AP和横向).
- 开发了一种两阶段的深度学习模型 (CNN与UNET架构),用于脊柱区域识别和曲率隔离.
- 该模型在200张图像上进行了训练,并在50张图像上进行了评估,使用Dice-Sørensen系数进行准确性评估.
主要成果:
- 人工智能模型实现了高细分精度,Dice-Sørensen系数为0.92 (AP) 和0.96 (横向).
- 该模型成功地细分了复杂的脊柱病理 (lordosis,脊柱结) 和脊柱仪器 (杆,螺丝) 的噪音.
- 该算法在各种成像场景中展示了强大的性能.
结论:
- 一个两阶段的深度学习模型在2D双平面放射图中准确地细分脊柱曲率.
- 基于UNET的AI提供了一个标准化和高效的工具,用于退行性脊椎病的评估.
- 这项技术有可能改善DS患者的临床决策和患者管理.
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