在MRI中使用机器学习的自动儿科TMJ关节磁盘识别和位移分类
Roxana Azma1, Abhilash Hareendranathan2, Mengxun Li3
1Mike Petryk School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Canada; Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta, Canada.
Journal of dentistry
|February 14, 2025
概括
一个由人工智能驱动的模型准确地解释儿科关节 (TMJ) MRI 扫描,识别解剖结构和分类磁盘位移. 这种人工智能工具有助于临床医生诊断儿童关节疾病.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 儿科放射学 儿科放射学
背景情况:
- 关节 (TMJ) 疾病在儿童中很常见.
- 精确解释TMJ磁共振成像 (MRI) 对于诊断至关重要.
- 当前的口译可能具有挑战性,特别是对于非专业人士来说.
研究的目的:
- 评估一个自动化的两步人工智能 (AI) 模型来解释儿科TMJ MRI.
- 评估模型识别TMJ骨结构和分类磁盘位移的能力.
- 为了确定人工智能模型的性能与人类专家相比.
主要方法:
- 一个深度学习模型 (UNet++) 在235名儿科患者的TMJMRI扫描上进行了训练和测试.
- 该模型自动细分TMJ解剖结构 (圆盘,圆柱体,关节突起).
- 一个自动化的算法根据地标分析对磁盘位移进行了分类.
主要成果:
- 人工智能模型在TMJ结构的细分方面取得了很高的性能 (磁盘的Dice系数为0.67,圆柱体的0.91).
- 自动磁盘位移分类的准确性与人类专家的准确性相当 (AUC 0.89-0.92).
- 在分析的关节中,有22%的关节表现出前盘位.
结论:
- 一个两步自动化的AI模型准确地识别TMJ骨结构,并在儿科TMJMRI中分类磁盘脱位.
- 这种人工智能工具可以帮助临床医生评估儿科TMJ疾病.
- 自动化TMJ-MRI解释软件可以提高儿童TMJ疾病的诊断准确性.
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