改进TMJ诊断:深度学习方法用于检测下带状骨变化
Kader Azlağ Pekince1, Adem Pekince1, Buse Yaren Kazangirler2,3
1Department of Oral and Maxillofacial Radiology, Karabuk University, Karabuk 78600, Turkey.
Diagnostics (Basel, Switzerland)
|May 1, 2025
概括
深度学习从全景X射线图中准确地检测下肌中的退行性骨变化. 这种人工智能方法有助于早期诊断关节 (TMJ) 疾病.
科学领域:
- 牙科和口腔卫生 牙科和口腔卫生
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 下尾骨的退行性骨变化是使用传统的全景放射图进行诊断的挑战.
- 早期发现这些变化对于及时干预和预防关节疾病进展至关重要.
研究的目的:
- 评估深度学习 (DL) 模型的有效性,以检测下骨中的退行性骨变化.
- 开发一种自动化的方法来识别诸如平面化,骨菌和侵蚀等条件在全景放射图上.
主要方法:
- 利用了来自全景射线图的3875个状体图像的数据集.
- 采用了各种深度学习架构 (DenseNets,ResNets,VGGNets,GoogleNets) 与转移学习.
- 使用70:30和80:20数据分割训练和测试模型,考虑骨变化的不同分类方法.
主要成果:
- 谷歌网架构实现了最高的准确率95.23%,正常平整变形数据集的80:20分割.
- 基于卷积神经网络 (CNN) 的方法在识别下尾骨异常方面表现出高的成功率.
- 该研究证实了DL在分类骨变化的有效性.
结论:
- 深度学习,特别是CNN,显示出从全景X射线图中准确有效地检测关节相关的状骨变化.
- 这种人工智能驱动的方法可以帮助临床医生识别需要进一步干预关节疾病的患者.
- 未来的研究应该探索横截面成像和结合训练,以提高诊断准确性和疾病管理.
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