时关节的多类细分使用集体深度学习
Kyubaek Yoon1, Jae-Young Kim2, Sun-Jong Kim3
1Department of Artificial Intelligence and Software, Ewha Womans University, Seoul, South Korea.
Scientific reports
|August 19, 2024
概括
深度学习模型在MRI扫描中准确地对关节 (TMJ) 组件进行细分. 这种人工智能工具可以改善对关节疾病的诊断,比如盘位移和骨关节炎.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 关节 (TMJ) 疾病会引起显著的面部或口腔不适.
- 准确的诊断依赖于TMJ组件的磁共振成像 (MRI) 分析.
- 图像质量的变化和复杂的解剖学挑战了精确的TMJ诊断.
研究的目的:
- 开发和验证深度学习模型,用于TMJ组件的自动细分 (关节骨,圆盘,骨).
- 评估人工智能增强工具在提高关节障碍诊断准确性的有效性.
主要方法:
- 开发了深度学习模型,用于在MRI中自动细分骨,圆盘和状骨.
- 在来自542名患者的3693张MRI扫描数据集上训练并验证了模型.
- 评估了一个组合模型,结合了五个单独的模型.
主要成果:
- 内部测试:组合模型实现了0.867 (骨),0.733 (圆盘),0.904 (圆柱状) 的子相似系数.
- 外部验证:实现了0.720 (骨),0.604 (圆盘),0.800 (圆柱体) 的子系数.
- 人工智能工具提高了医生诊断的准确性,特别是在区分前盘位移和骨关节炎方面.
结论:
- 使用深度学习的TMJ自动细分是一种有前途的方法.
- 人工智能驱动的细分有助于完善关节疾病的诊断和治疗.
- 增强的诊断能力可以导致改善TMJ条件的患者管理.
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