使用单模和多模深度学习模型的MRI和CT扫描来预测骨质疏松症
Yasemin Küçükçiloğlu1,2, Boran Şekeroğlu3, Terin Adalı2,4,5
1Near East University Faculty of Medicine, Department of Radiology, Nicosia, Cyprus
Diagnostic and interventional radiology (Ankara, Turkey)
|June 13, 2023
概括
深度学习模型使用磁共振 (MR) 和计算机断层扫描 (CT) 成像准确预测骨质疏松症. 结合MRI和CT扫描的多模式方法显著改善了骨质疏松症预测的准确性.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 骨质疏松症是一种全身骨退化,显著影响生活质量和死亡率.
- 准确的骨质疏松症预测对于降低风险和患者管理至关重要.
- 深度学习模型在分析医疗图像用于诊断目的方面表现有前途.
研究的目的:
- 开发和评估单模和多模深度学习模型,用于预测腰椎骨矿物质损失.
- 为了利用磁共振 (MR) 和计算机断层扫描 (CT) 成像来预测骨质疏松症.
- 通过放射性扫描来评估深度学习在诊断骨质疏松症方面的有效性.
主要方法:
- 开发具有双块的单模和多模卷积神经网络 (CNN).
- 利用了来自双能X射线吸收计 (DEXA) 和MR/CT扫描的患者的数据集.
- 将拟议的CNN模型与标准CNN和6个预训练的深度学习模型进行比较.
主要成果:
- 拟议的单模模型实现了96.54% (MRI),98.84% (CT) 和96.76% (组合) 的平衡精度.
- 多模式模型在5倍交叉验证中实现了98.90%的平衡精度.
- 模型表现出高性能,具有有效的特征提取,优于基准模型.
结论:
- 深度学习模型使用MRI和CT图像准确预测骨质疏松症.
- 综合MRI和CT数据的多模式方法提高了骨质疏松症预测的准确性.
- 为了潜在的临床实施,需要进行进一步的前性研究.
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