基于YOLO v11的深度学习方法,用于自动检测囊
Ömer Faruk Kaygısız1, Ömer Uranbey2, Burcu Gürsoytrak2
1Independent Researcher, Oral and Maxillofacial Surgeon, Gaziantep, Türkiye, Turkey. dtkaygisiz@gmail.com.
BMC oral health
|October 2, 2025
概括
使用YOLO v11的AI系统在全景放射图上准确地检测下囊. 这种人工智能工具显示了改善牙科诊断的希望,通过识别诸如牙囊,牙生囊和根性囊等病变.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 囊是一种常见的放射性牙病变,具有相似的放射性外观,构成诊断挑战.
- 精确识别下巴囊,包括牙囊 (DCs),牙生囊 (OKCs) 和根囊 (RCs),对于有效的治疗至关重要.
研究的目的:
- 开发和评估一种基于人工智能的系统,用于使用YOLO v11深度学习模型检测和分类囊.
- 评估YOLO v11在全景放射图上区分不同类型的囊的性能.
主要方法:
- 训练了YOLO v11 小型模型在311个增强全景放射图 (211个囊性,100个正常) 上.
- 评估了多类 (DC,OKC,RC) 和单类 (囊检测) 配置.
- 使用精度,回忆,F1得分和平均平均精度 (mAP) 测量性能.
主要成果:
- 多类YOLO v11型号实现了86%的mAP,F1得分为83%.
- 分类精度很高:DC的91%,OKC的85%,RC的82%.
- 单级车型实现了84%的mAP和81%的F1得分.
结论:
- YOLO v11在从全景放射图中检测和分类囊中表现出显著的准确性.
- 人工智能系统具有很强的潜力,可以帮助牙科专业人员诊断囊.
- 建议对更大,更平衡的数据集进行进一步的研究,以提高模型的通用性.
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