基于深度学习的全景射线图的自动诊断图表:YOLOv11和YOLOv12的比较
Onur Mutlu1, Elif Aslan2, Ali Mert3
1Faculty of Science, Department of Computer Science, Karadeniz Technical University, Trabzon, Türkiye. onurmutlu@ktu.edu.tr.
Odontology
|February 16, 2026
概括
在全景放射图上检测13种牙科疾病时,YOLOv11的性能优于YOLOv12. 这种深度学习模型显示出强大的概括性,为改善牙科诊断和临床效率提供了可靠的工具.
科学领域:
- 人工智能在牙科中的应用
- 对于医学成像的深度学习
- 放射性诊断支持支持 放射性诊断支持
背景情况:
- 在全景射线图上进行自动诊断绘图对于优化工作流程和减少诊断变化至关重要.
- 下一代深度学习架构为增强的自动牙状况检测提供了潜在的潜力.
研究的目的:
- 为了比较分析YOLOv11和YOLOv12深度学习架构的性能,用于检测13种牙科疾病.
- 评估这些模型在内部和外部数据集上的概括能力.
主要方法:
- 使用了2,297张全景放射图的混合数据集.
- 模型被训练来检测包括,植入物,骨损失和冲击在内的条件.
- 使用mAP@0.5,精度,回忆和F1评分来评估性能.
主要成果:
- YOLOv11在内部测试组中获得了0.857的优异mAP@0.5,在外部测试组中获得了0.806.
- YOLOv11表现出比YOLOv12更强大的性能和概括性.
- 对于明显的物体 (皇冠,植入物) 的检测准确度高于微妙的病理 (骨损失,).
结论:
- 与YOLOv12相比,YOLOv11是一个更可靠的架构,用于多类牙科物体检测.
- YOLOv11模型显示出作为临床决策支持工具的巨大潜力,以提高诊断准确性和效率.
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