使用基于YOLO的深度学习模型在周边放射图中检测外部根吸收
S T Gokdeniz1, A Buyuksungur2, M E Kolsuz1
1Ankara University, Faculty of Dentistry, Dentomaxillofacial Radiology Department, Ankara, 06000, Turkey.
Dento maxillo facial radiology
|September 27, 2025
概括
人工智能 (AI) 算法在检测外部根吸收,一种破坏性牙状况方面取得了很高的成功. 这种人工智能驱动的方法有助于早期诊断,可能会防止由于组织损失而导致牙提取.
科学领域:
- 牙科 牙科是指牙科的专业.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 外部根再吸收是一种破坏性牙疾病,通常无症状,导致牙组织的显著损失和潜在的提取.
- 早期诊断至关重要,但由于缺乏症状,具有挑战性.
研究的目的:
- 开发和评估人工智能 (AI) 算法,以协助诊断外部根吸收.
- 评估YOLOv5x-cls和YOLOv5x-seg模型在检测和细分外部根吸收的有效性.
主要方法:
- 110颗拔出的牙经过化学去矿化,并使用放射结合物幻影模型进行成像.
- 创建了一个数据集,包括584张周围腹部放射图.
- 用YOLOv5x-cls和YOLOv5x-seg模型进行外部根吸收检测和细分.
主要成果:
- YOLOv5x-cls模型在外部根吸收的分类中获得了1.0的F1得分,这表明其准确性很高.
- YOLOv5x-seg模型在细分外部根吸收方面获得了0.8593的F1得分,证明了有效的性能.
- 分类模型的性能优于细分模型.
结论:
- 人工智能算法在使用幻影模型检测外部根吸收时表现出高的成功率.
- 本研究提出了一种创新的,可靠的AI驱动的方法,用于对外部根吸收的放射性评估.
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