牙编号与多边形细分在周围的X线图:一个人工智能研究研究
Halil Ayyıldız1,2, Mukadder Orhan3, Elif Bilgir4
1Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kutahya Health Science University, Kutahya, Türkiye. hayyil2@uic.edu.
Clinical oral investigations
|October 24, 2024
概括
使用YOLOv5的人工智能在检测和编号牙上表现出高准确度. 这项技术可以帮助牙医,提高诊断速度和减少错误,以改善患者护理.
科学领域:
- * 牙科放射学 牙科放射学
- * 医学中的人工智能
- * 机器学习应用程序
背景情况:
- *准确的牙识别和编号对于牙科诊断和治疗计划至关重要.
- * 传统的牙识别方法可能耗时且容易出现错误,特别是对于缺乏经验的临床医生来说.
- *人工智能 (AI) 为自动化和提高放射分析准确度提供了潜在的解决方案.
研究的目的:
- *为了评估YOLOv5的有效性,一个AI模型,在自动检测和编号牙在周围的X射线图.
- *为了验证YOLOv5可以在这个任务中实现高精度的假设.
- * 评估人工智能在增强牙科放射学解释方面的潜力.
主要方法:
- * 采用了6446张匿名的周周放射图的数据集.
- *图像被随机分配到培训 (80%),验证 (10%) 和测试 (10%) 组.
- * 模型性能使用混矩阵和关键性能指标进行评估.
主要成果:
- *YOLOv5在测试期间实现了94.4%的真阳性率,4.4%的假阳性率和1.2%的假阴性率.
- *高回忆率 (0.9882),精度 (0.9548) 和F1得分 (0.9712) 表示强大的性能.
- * 该模型在ROC曲线上实现了0.603的曲线下的面积 (AUC).
结论:
- *YOLOv5在牙周放射学中显示出近乎完美的牙编号性能.
- *像YOLOv5一样,人工智能可以作为牙医的宝贵指南,提高诊断准确性和效率.
- *这项技术有可能加快放射检查,减少缺乏经验的牙医的错误,并有助于牙科教育.
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