一个深度学习模型,用于与受影响的第三关节相关的第二关节病变
Yujia Jiang1, Jiayi Jin2, Yunru Gao2
1School of Stomatology, Nanjing Medical University, Nanjing, Jiangsu, China.
International dental journal
|March 1, 2026
概括
一个新的深度学习系统,SMM-YOLOv8n,准确地检测病理,如牙损伤和根吸收在第二牙邻受影响的第三牙在牙科放射. 这种工具提高了诊断速度和准确性,有助于治疗计划.
科学领域:
- 人工智能在牙科中的应用
- 医学成像分析 医学成像分析
- 口腔病理检测检测口腔病理检测
背景情况:
- 受影响的第三 (ITM) 可以导致邻近的第二 (M2s) 病理.
- 在全景放射图上准确检测这些M2病理对于及时干预至关重要.
- 当前的诊断方法可能在速度和准确性方面存在局限性.
研究的目的:
- 开发和评估一个自动化的深度学习 (DL) 系统,用于检测和分类与ITM相关的M2病理.
- 与现有方法相比,提高诊断准确性和效率.
- 支持临床决策管理ITM相关并发症.
主要方法:
- 一个包含1170张全景放射图的数据集,其中M2与ITM相邻,是策划的.
- 图像被分为四组:没有病变,,外根再吸收 (ERR),或两者兼而有之.
- 基于具有注意力机制的YOLOv8的增强型SMM-YOLOv8n模型被开发并使用转移学习进行训练.
- 该模型使用5倍交叉验证进行了评估.
主要成果:
- 该SMM-YOLOv8n模型实现了0.886.6的平均平均精度 (mAP@50).
- 宏观平均精度,回忆和F1分数分别为0.894,0.96和0.926.
- 与基线YOLOv8相比,该系统表现出显著的改进,增加了临床医生的敏感性和减少了解释时间.
结论:
- 该SMM-YOLOv8n系统可在全景X射线图上准确有效地检测ITM相关的M2病理.
- 这种DL工具可以提高早期诊断和治疗规划.
- 它可以减少对形束计算机断层扫描进行初步评估的依赖,并作为一个有价值的决策支持系统.
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