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口腔卫生中的深度学习:通过YOLO框架自动检测牙斑,并使用O'Leary指数进行量化
Alfonso Ramírez-Pedraza1,2, Sebastián Salazar-Colores3, Crystel Cardenas-Valle4
1Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, Mexico.
Diagnostics (Basel, Switzerland)
|January 25, 2025
概括
先进的AI模型现在可以检测牙斑的不同阶段,改善早期口腔疾病干预. 这种自动化方法有望改善口腔健康,特别是在服务不足的地区.
科学领域:
- 口腔健康 口腔健康
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 牙和牙周炎等口腔疾病在全球普遍存在,通常源于牙斑.
- 早期发现斑块阶段对于及时干预和疾病预防至关重要.
- 目前的斑块评估方法通常依赖于手动视觉检查,这可能是主观的,耗时的.
研究的目的:
- 评估最先进的YOLO (You Only Look Once) 对象检测架构的有效性,用于识别牙斑块的三个不同阶段:新,成熟和过度成熟.
- 为了比较不同YOLOv9,YOLOv10和YOLOv11变体在不同成像条件下的斑块检测性能.
- 评估自动斑块检测的潜力,以提高早期诊断和减少口腔疾病的负担.
主要方法:
- 在斑块披露后,使用移动设备收集了来自177个人的531张RGB图像的数据集.
- 图像经过预处理以进行照明和颜色正常化.
- 训练了YOLOv9,YOLOv10和YOLOv11模型来检测和分类斑块阶段,性能指标包括精度,回忆和mAP@50.
主要成果:
- YOLOv11m在0.713的50%IoU (mAP@50) 实现了最高的平均精度,在检测过度成熟的斑块方面表现出卓越的性能.
- 较旧的斑块阶段通常比较新的斑块更准确地检测到,这可能是具有挑战性的,因为它与牙组织的相似性.
- 使用O'Leary指数的分析显示,超过一半的研究参与者有严重的斑块水平.
结论:
- 先进的YOLO模型可用于在各种成像场景中自动检测牙斑.
- 这种人工智能驱动的方法有可能简化临床工作流程,提高早期口腔疾病诊断的准确性.
- 自动化斑块检测可以大大减轻口腔健康问题,特别是在资源有限的环境中.
相关概念视频
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Mouth Inspection
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