有注释的口内图像数据集用于检测牙虫
Syed Muhammad Faizan Ahmed1, Muhammad Huzaifa Ghori1, Aamna Khalid1
1Section of Dentistry, Department of Surgery, The Aga Khan University, Karachi, Pakistan.
Scientific data
|July 27, 2025
概括
这项研究介绍了人工智能 (AI) 检测牙的第一个公共口腔内图像数据集. YOLOv8s实现了最高的性能,推进了人工智能驱动的诊断.
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:
- 缺乏公开可用的注释型口腔内图像数据集,阻碍了用于检测牙损伤的AI开发.
- 牙损是一个普遍存在的全球健康问题,需要有效的诊断工具.
研究的目的:
- 引入第一个公开可用的注释型口腔内图像数据集,用于人工智能驱动的牙虫检测.
- 为人工智能模型在检测牙损伤方面的性能建立一个基准.
主要方法:
- 收集了来自巴基斯坦10-24岁个体的6313张口内图像.
- 使用LabelMe进行注释的图像,经牙医验证,并转换为YOLO,PASCAL VOC和COCO格式.
- 训练并评估了五种人工智能模型 (YOLOv5s,YOLOv8s,YOLOv11,SSD-MobileNet-v2,更快的R-CNN). 在这项研究中,我们发现了
主要成果:
- 数据集包括混合和永久牙的各种口内视图.
- YOLOv8s表现出最好的性能,平均平均精度 (mAP) 为0.841 在0.5 交叉点与联盟 (IoU) 上.
结论:
- 开发的数据集和人工智能模型基准显著推进基于人工智能的牙科诊断以检测.
- 未来的研究应该包括初级牙和各种成像设备.
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