在全景放射图上对牙科创伤的自动细分中,YOLO模型的比较性能
Derya Sarıoğlu1, Zehra Güner1, Saadet Nilsu Muttepe1
1Department of Paediatric Dentistry, Faculty of Dentistry, Gaziantep University, Gaziantep, Türkiye.
概括
人工智能模型YOLOv12在儿童中检测牙创伤方面表现有前途.
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
- 儿科牙科创伤分析
背景情况:
- 创伤性牙损伤在儿童中很常见.
- 牙创伤的准确诊断对于有效治疗至关重要.
- 全景放射是牙科创伤评估的标准成像技术.
研究的目的:
- 为了比较YOLOv8,YOLOv11和YOLOv12深度学习模型的性能.
- 为了评估它们在细分牙损伤的有效性,在儿科全景放射图上.
- 确定最佳的人工智能模型用于儿童的牙创伤检测.
主要方法:
- 利用了患有牙创伤的儿科患者 (6-13岁) 的全景放射图.
- 使用了一个AI模型 (CranioCatch) 与YOLOv8,YOLOv11和YOLOv12架构.
- 进行了牙损伤的自动检测和分类.
主要成果:
- 在硬组织损伤 (0.73) 和软组织损伤 (0.69) 方面,YOLOv12实现了最高的曲线下面积 (AUC).
- 此外,YOLOv12也获得了最高的多类F1分数 (0.683).
- 所有模型在区分硬组织损伤方面都表现得更好,YOLOv12显示了最一致的结果.
结论:
- YOLOv12深度学习模型在从全景射线图中检测儿科牙创伤方面表现出卓越的性能.
- 目前在牙科创伤学中人工智能的表现还不是完美的.
- 未来的AI开发需要与专家牙医密切合作,以改善临床决策支持.
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