检测破碎的内牙仪器在周围的X线图:YOLOv8和面具R-CNN的比较研究
İrem Çetinkaya1, Ekin Deniz Çatmabacak1, Emir Öztürk2
1Department of Endodontics, Faculty of Dentistry, Trakya University, Edirne 22030, Turkey.
Diagnostics (Basel, Switzerland)
|March 28, 2025
概括
人工智能模型,YOLOv8和Mask R-CNN,在X光照中准确地检测破碎的内牙仪器和根管治疗,匹配专家内牙医的性能.
科学领域:
- 牙科放射学 牙科放射学
- 人工智能在医学中的应用
- 医学图像分析 医学图像分析
背景情况:
- 精确定位骨折的内牙仪器 (FEI) 在周围放射 (PAs) 中至关重要,但具有挑战性.
- 检测FEI和根管治疗 (RCT) 的现有方法需要提高临床效率.
研究的目的:
- 评估YOLOv8和Mask R-CNN在PA中检测FEI和RCT的性能.
- 将这些人工智能模型的诊断能力与经验丰富的内牙科医生进行比较.
主要方法:
- 用于培训和评估,使用了1050个注释PA的数据集.
- 评估了YOLOv8和Mask R-CNN模型的准确性,IoU,mAP50和推断时间.
- 人工智能预测与人类注释进行了比较,并分析了观察者一致性.
主要成果:
- YOLOv8实现了97.40%的准确性和98.9%的mAP50,推断时间为14.6毫秒.
- 面具R-CNN实现了98.21%的准确性和95%的mAP50,推断时间为88.7毫秒.
- 人工智能模型和经验丰富的内牙科医生的诊断性能之间没有发现统计学上显著的差异.
结论:
- 无论是YOLOv8还是Mask R-CNN都表现出高的诊断准确性和可靠性,与内牙科医生相美.
- 由于其速度,YOLOv8适合实时应用;Mask R-CNN在细分方面表现出色.
- 人工智能在牙科诊断中的整合显示出改善临床结果的前景.
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