在全景放射图中基于深度学习检测动脉动脉瘤
Thais Martins Jajah Carlos1, Márcio José da Cunha1, Aniel Silva Morais1
1Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38408-100, Brazil.
Bioengineering (Basel, Switzerland)
|January 28, 2026
概括
全景牙科放射能可以检测动脉化,这是中风风险标志物. 一个深度学习模型 (MobileNetV2) 在识别这些化的过程中取得了很高的准确性,支持针对血管风险的机会性查.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 心血管健康 心血管健康
背景情况:
- 在C3-C4脊椎水平的全景牙科放射图上经常可见的动脉结,与中风风险增加有关.
- 早期识别这些化对于评估系统性血管风险至关重要.
研究的目的:
- 开发和评估使用MobileNetV2进行深度学习的方法,以在全景放射图上自动识别动脉动脉瘤.
- 评估人工智能辅助全景放射学在机会性中风风险查方面的潜力.
主要方法:
- 一个包含378个感兴趣地区 (ROI) 图像的数据集进行了预处理和增强.
- 采用了两阶段的深度学习培训计划,其中包含类权重和早期停止.
- 移动NetV2模型经过训练和验证,在一个独立的测试集上评估性能.
主要成果:
- 人工智能模型在测试组中实现了高性能指标:94.7%的准确性,95.7%的灵敏性,94.1%的特异性,AUC为0.963.
- 一个针对灵敏度的值 (82.6%) 产生了91.2%的准确性和97.1%的特异性.
- 该模型在识别动脉动脉瘤方面表现出强的表现.
结论:
- 全景射线图可以作为查系统性血管风险的机会性工具.
- 人工智能辅助的方法显示,在预防性医疗保健策略中,能够提前识别动脉动脉瘤的巨大潜力.
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