通过人工智能辅助在全景放射图上识别凝结性骨炎和异常性骨质硬化
Ibrahim Burak Yuksel1, Amin Boudesh2, Masoud Ghanbarzadehchaleshtori3
1Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Necmettin Erbakan University, Konya, Turkey.
Scientific reports
|August 11, 2025
概括
新的人工智能模型YOLOv11和YOLOv8在牙科X射线上准确检测异常性骨质硬化 (IOS) 和凝结性骨炎 (CO). YOLOv11表现出卓越的性能,改善了对这些病变的诊断一致性和治疗计划.
科学领域:
- 口腔和面放射学 口腔和面放射学
- 人工智能在牙科中的应用
- 医学成像分析 医学成像分析
背景情况:
- 异常性骨质硬化症 (IOS) 和凝结性骨炎 (CO) 是具有相似放射性外观的放射性病变,使诊断复杂化.
- 准确识别IOS和CO对于适当的患者管理和避免不必要的侵入性手术至关重要.
研究的目的:
- 评估YOLOv8和YOLOv11深度学习算法的诊断性能,用于在全景放射图上识别IOS和CO.
- 为了比较YOLOv11与YOLOv8在检测这些特定的辐射不透明下巴病变方面的疗效.
主要方法:
- 1000张全景放射图的数据集被两个口腔和面放射科医生追溯收集并用边界框进行注释.
- 图像被标准化并分为培训 (70%),验证 (15%) 和测试 (15%) 集,用于模型开发和评估.
- 包括准确度,灵敏度,精度,F1得分和AUC在内的性能指标被用于评估算法的诊断能力.
主要成果:
- YOLOv11显示了高精度 (98.8%的IOS, 97.1%的CO) 和F1得分 (96.8%的IOS, 95.6%的CO).
- YOLOv8取得了强的结果,精度得分为96.6% (IOS) 和91.4% (CO),F1得分为94% (IOS) 和90% (CO).
- 这两种AI模型都被证明能够准确识别IOS和CO病变.
结论:
- 由人工智能驱动的深度学习模型,特别是YOLOv11,可以在全景放射图上有效地识别异常性骨质硬化和凝结性骨炎.
- 这些人工智能工具有可能提高诊断准确性,减少侵入性手术,并在临床牙科实践中优化治疗计划.
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