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使用深度学习方法开发和验证股骨头骨髓缩的自动分类系统:一个多中心研究

Xianyue Shen¹, Ziling He², Yi Shi³

¹Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin province, PR China.

The Journal of arthroplasty

|August 12, 2023

概括

一个深度学习模型准确地分类了股骨头骨硬化 (ONFH) 的严重程度. 这种卷积神经网络 (CNN) 有助于预测股骨头崩,并指导ONFH患者的临床决策.

科学领域:

整形外科整形外科整形外科
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

足骨头骨 necrosis (ONFH) 的准确分类对于及时干预至关重要.
当前的分类方法可能会从先进的计算方法中受益.

研究的目的:

开发和验证一个深度学习模型来对ONFH进行分类.
评估模型的性能与专家评价的对比.

主要方法:

从1,337个部进行的1,806个中冠膜MRI的回顾性分析.
使用培训/验证/测试分割 (7:1:2) 开发一个卷积神经网络 (CNN).
使用独立数据集进行外部验证,并与骨科外科医生进行比较.

主要成果:

在内部验证中,CNN的整体准确率达到87.8%,在外部验证中达到83.8%.
高宏观平均AUC (0.90内部,0.87外部) 和性能指标 (精度,回忆,F值).
美国有线电视新闻网 (CNN) 的表现与副首席整形外科医生的表现相当或更高.

结论:

一个CNN模型是可行的和强大的ONFH分类,包括死角区域定位.
ONFH的深度学习分类提供了高准确性和通用性.
这种方法可以改善大腿骨头崩的预测,并支持临床决策.

关键词:

股骨头头部骨质死结.这是分类分类的分类.深度学习是一种深度学习.肌肉骨疾病肌肉骨疾病

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