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使用基于MRI的深度学习放射学来诊断与轴性脊柱关节炎相关的神经炎
Ke Zhang1, Chaoran Liu2, Jielin Pan3
1Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China.
European journal of radiology
|February 7, 2024
概括
一个集成MRI和临床数据的深度学习放射学模型显著改善了轴性脊柱关节炎 (axSpA) 中的圣骨炎的诊断. 这种综合方法为识别 axSpA 相关的圣炎提供了出色的诊断性能.
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
- 风湿病学和免疫学
背景情况:
- 轴脊椎关节炎 (axSpA) 是一种慢性炎症性疾病,主要影响轴骨架.
- 圣炎,圣关节的炎症,是axSpA.的一个关键诊断特征.
- 精确及及早诊断圣骨炎对于及时治疗和管理axSpA至关重要.
研究的目的:
- 评估一个深度学习放射学 (DLR) 模型,用于诊断axSpA中的圣炎.
- 整合多模式MRI功能和临床信息,以提高诊断准确度.
- 将DLR模型的性能与传统方法进行比较.
主要方法:
- 在485名患有圣炎 (n=288) 或非圣炎 (n=197) 的患者的回顾性分析中,使用了圣关节 (SIJ) 的MRI.
- 开发一个3D U-Net CNN用于自动化SIJ细分.
- 使用ResNet50,ResNet101和DenseNet121进行单模诊断,通过组合方法集成以创建DLR签名.
- 一个组合模型将DLR签名与使用多变量逻辑回归的临床因素结合起来.
主要成果:
- 最佳单模式模型 (ResNet50) 的AUC值为0.839,准确度为0.804.
- 结合DLR特征和临床因素的组合模型表现出卓越的性能,AUC为0.910和准确度为0.856.
- 组合模型显著优于最佳组合模型 (AUC:0.868;精度:0.825) (P <0.05).
- 决策曲线分析 (DCA) 表明组合模型具有良好的临床效用.
结论:
- 结合DLR签名和临床因素的诊断模型在诊断axSpA相关的圣炎方面取得了卓越的表现.
- 这种综合方法提供了一个有前途的工具,可以提高 axSpA 中神经炎的诊断准确性.
- 需要进一步验证以确定其临床适用性.
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