基于关键点区域的卷积神经网络的开发和验证,以自动化使用全脊柱站立放射测量
Mert Marcel Dagli1, Jonathan H Sussman2, Jaskeerat Gujral2
1Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. marcel.dagli@pennmedicine.upenn.edu.
Acta neurochirurgica
|August 22, 2025
概括
一个使用KR-CNN的AI工具准确地测量了青少年特异性脊椎病 (AIS) 查和结果评估的胸部科布角. 这项技术有助于早期发现和治疗脊椎形.
科学领域:
- 整形医学
- 医学成像
- 人工智能
背景情况:
- 青少年特异性脊柱病 (AIS) 导致脊柱形,诊断和结果评估依赖于X射线的科布角测量.
- 目前的查方法可能缺乏专家解释,可能会延迟早期干预.
研究的目的:
- 在AIS中开发和验证人工智能 (AI) 工具用于自动胸部科布角 (TCA) 测量.
- 提高AIS查和术后结果评估的准确性和效率.
主要方法:
- 一个基于关键点区域的卷积神经网络 (KR-CNN) 已被开发用于自动化TCA测量.
- 在KR-CNN模型上训练了609个AIS患者的整体脊柱放射图.
- 使用手术前和手术后X射线对83名AIS患者进行了验证.
主要成果:
- 在KR-CNN模型中,平均绝对误差 (MAE) 为2.22,类内相关系数 (ICC) 为0.98.
- 性能指标包括平均平方误差 (MSE) 为9.1和对称平均绝对误差 (SMAPE) 为4.29.
- 与现有方法相比,人工智能工具表现出更高的性能.
结论:
- 开发的AI方法可以快速准确地选AIS.
- 它有助于对AIS患者的术后结果进行准确评估.
- 这为自动化脊髓皮层测量和脊髓成像的进一步人工智能开发提供了基础.
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