预先的体外研究和深度学习算法的应用在形束计算机断层扫描图像植入物识别中的初步体外研究和应用
Shaobo Ou-Yang1, Shuqin Han1, Dan Sun2
1The Affiliated Stomatological Hospital of Nanchang University, The Key Laboratory of Oral Biomedicine, Jiangxi Province Clinical Research Centre for Oral Diseases, Nanchang, Jiangxi Province, China.
Scientific reports
|October 27, 2023
概括
准确识别牙科植入物对于维修和维护至关重要. 深度学习算法,特别是ResNet152V2,在对植入物3D圆束计算机断层扫描 (CBCT) 图像进行分类时显示出高精度.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 精确识别牙科植入物 (骨组织植入物取代天然牙根) 对于有效的修复和维护至关重要.
- 深度学习卓越于图像分析,为自动识别和分类任务提供了潜力.
研究的目的:
- 评估各种深度学习算法在识别和分类三维 (3D) 圆束计算机断层扫描 (CBCT) 牙科植入物图像中的有效性.
- 确定基于人工智能的牙科植入物识别系统的最佳性能算法.
主要方法:
- 获得了27种不同品牌和大小的牙科植入物3D CBCT成像数据.
- 将3D数据处理成一个由13,500张二维图像组成的数据集.
- 应用了九个深度学习算法 (GoogleNet,InceptionResNetV2,InceptionV3,ResNet50,ResNet50V2,ResNet101,ResNet101V2,ResNet152,ResNet152V2) 来进行图像分类,并使用精度,混矩阵,ROC曲线,AUC,模型参数和训练时间来评估性能.
主要成果:
- 所有九个深度学习算法在植入物识别中都表现出令人满意的性能.
- ResNet152V2实现了最高的测试准确性 (99.3%),分类准确性和曲线下面积 (AUC) 值 (1.00).
- 该研究在评估的算法中实现了高训练准确率 (高达100%) 和测试准确率 (高达99.3%).
结论:
- 深度学习算法,特别是ResNet152V2,对于从3D CBCT图像中准确识别牙植入物品牌和规格非常有效.
- 使用ResNet152V2的AI识别系统提供了高效,准确,稳定和低成本的牙科植入物识别.
- 这项技术可以显著帮助精确修复和维护牙科植入物.
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