基于YOLO-V5的深度学习方法用于牙检测和细分,用于混合牙的儿科全景放射图
Busra Beser1, Tugba Reis2, Merve Nur Berber1
1Department of Orthodontics, Faculty of Dentistry, Recep Tayyip Erdogan University, Rize, Turkey.
BMC medical imaging
|July 11, 2024
概括
这项研究表明,YOLO-v5人工智能模型可以准确地检测和细分牙在全景X线图,为儿科患者混合牙,提高诊断准确度.
科学领域:
- 人工智能在牙科中的应用
- 放射图像分析 放射图像分析
- 儿科牙科诊断 儿科牙科诊断
背景情况:
- 在全景放射图 (PRs) 中精确识别和编号牙对于儿童牙科的正确诊断至关重要.
- 混合牙在放射性解读方面存在挑战,因为它存在着落叶牙和永久牙.
研究的目的:
- 评估YOLO-v5人工智能 (AI) 算法的有效性,用于在PRs中自动检测,细分和编号牙.
- 评估YOLO-v5在分析儿科患者混合牙的性能.
主要方法:
- 来自混合牙的儿科患者的3854个PR的数据集被策划和标记.
- 数据集被分为培训 (80%),验证 (10%) 和测试 (10%) 的子集.
- 基于YOLO-v5的AI模型被开发并训练用于牙检测和细分.
主要成果:
- 在牙检测方面,YOLO-v5实现了高性能,灵敏度,精度和F1得分为0.99,mAP-0.5为0.98.
- 对于牙细分,YOLO-v5显示了灵敏度,精度和F1得分为0.98,mAP-0.5为0.98.
结论:
- 基于YOLO-v5的AI模型显示了在PRs中精确检测和细分叶牙和永久牙的巨大潜力.
- 这项技术可以帮助提高混合牙的儿科患者牙科诊断的效率和准确性.
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