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基于细分的多类检测和X射线绘制的牙周和修复条件在咬牙X射线图使用深度学习
Ali Batuhan Bayırlı1, Buse Kesgin2, Mehmetcan Uytun1
1Department of Periodontology, Faculty of Dentistry, Mugla Sıtkı Kocman University, 48000 Mugla, Türkiye.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
这项研究开发了一种使用YOLOv8x-seg的AI模型,用于检测咬牙翼放射图上的八种牙科疾病,在牙周问题如骨损失方面达到高精度,在修复性问题方面取得中等成功.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 咬牙X射线图对于评估牙腐烂,修复和牙周状况至关重要.
- 现有的牙科成像人工智能研究往往忽略了咬牙翼放射图上的多类诊断图表.
研究的目的:
- 开发和评估一个深度学习模型 (YOLOv8x-seg),用于同时检测八个牙周和修复性参数.
- 评估AI模型在不同牙科疾病中的诊断性能.
主要方法:
- 专家对1197张数字咬翼放射图的数据集进行了注释,为八种条件提供了7860个标签.
- 在80%的数据上训练了YOLOv8x-seg模型,在10%的数据上进行了验证和测试,使用了广泛的数据增强.
- 使用精度,回忆,F1得分和混矩阵来评估性能.
主要成果:
- 该模型实现了膜骨损失 (F1:0.88) 的高精度.
- 对于牙结石 (F1: 0.58) 和 (F1: 0.57) 观察到适度的表现.
- 较低的检测率被注意到较少频繁的条件,如宫边缘隙 (F1: 0.23) 和二次 (F1: 0.29).
结论:
- YOLOv8x-seg模型在检测牙周状况方面取得了很高的成功,在咬伤翼放射图上检测恢复性参数方面取得了中等的成功.
- 这种AI框架为同时评估多个牙科发现提供了一种可行的方法,尽管性能因条件频率而异.
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