使用MobileNet架构对植入物桥关系进行分类
Hao-Chieh Chang1, Li-Wen Yu1,2, Bo-Yi Liu3
1Graduate Institute of Clinical Dentistry, School of Dentistry, College of Medicine, National Taiwan University, Taipei, Taiwan.
Journal of dental sciences
|February 2, 2024
概括
移动网准确地使用圆束CT扫描来分类牙植入物 - 脊柱关系. 这种人工智能方法有助于术前诊断和牙科植入物治疗计划,即使数据有限.
科学领域:
- 牙科植入物学 牙科植入物学
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 准确的植入物分类对于有效的牙科植入物治疗计划至关重要.
- 深度学习模型为自动化这种分类过程提供了潜力.
研究的目的:
- 评估MobileNet深度学习模型在识别牙科植入物和骨之间的关系方面的有效性.
- 评估MobileNet在不同数据集大小和预处理条件中的性能.
主要方法:
- 使用了630个形束计算机断层扫描 (CBCT) 切片从412名患者,根据Terheyden的定义手动分类.
- 在有限的 (219个切片) 和完整的数据集 (630个案例) 上训练了MobileNet,有和没有自动填空.
主要成果:
- 在有限的数据 (条件A) 中,获得了84.00%的整体准确性.
- 使用完整数据集 (条件B和C) 达到95.28%的准确性.
- 条件C显示高精度 (94.00-99.21%) 和F1得分 (89.36-100.00%),消除了分类错误.
结论:
- 移动网络有效地识别了CBCT图像上的植入物分类.
- 人工智能辅助分类可以支持临床医生在手术前诊断和牙科植入物治疗计划.
- 这种技术适合在一般牙科实践环境中实施.
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