Researcher, Attending Physician, Department of Oral and Maxillofacial Surgery, Rambam Health Care Campus, Haifa, Israel.
Lecturer, Department of Applied Physics/Electro-optics Engineering, The Jerusalem College of Technology, Jerusalem, Israel.
Oral Surg Oral Med Oral Pathol Oral Radiol. 2020 Nov;130(5):593-602. doi: 10.1016/j.oooo.2020.05.012. Epub 2020 Jun 3.
The aim of this study was to develop a computer vision algorithm based on artificial intelligence, designed to automatically detect and classify various dental restorations on panoramic radiographs.
A total of 738 dental restorations in 83 anonymized panoramic images were analyzed. Images were automatically cropped to obtain the region of interest containing maxillary and mandibular alveolar ridges. Subsequently, the restorations were segmented by using a local adaptive threshold. The segmented restorations were classified into 11 categories, and the algorithm was trained to classify them. Numerical features based on the shape and distribution of gray level values extracted by the algorithm were used for classifying the restorations into different categories. Finally, a Cubic Support Vector Machine algorithm with Error-Correcting Output Codes was used with a cross-validation approach for the multiclass classification of the restorations according to these features.
The algorithm detected 94.6% of the restorations. Classification eliminated all erroneous marks, and ultimately, 90.5% of the restorations were marked on the image. The overall accuracy of the classification stage in discriminating between the true restoration categories was 93.6%.
This machine-learning algorithm demonstrated excellent performance in detecting and classifying dental restorations on panoramic images.
本研究旨在开发一种基于人工智能的计算机视觉算法，旨在自动检测和分类全景图像上的各种牙体修复体。
对 83 张匿名全景图像中的 738 个牙体修复体进行了分析。图像自动裁剪以获得包含上颌和下颌牙槽嵴的感兴趣区域。然后，使用局部自适应阈值对修复体进行分割。将分割后的修复体分为 11 类，并对算法进行训练以对其进行分类。基于算法提取的灰度值形状和分布的数值特征用于将修复体分类到不同的类别中。最后，使用带有纠错输出码的三次支持向量机算法和交叉验证方法，根据这些特征对修复体进行多类分类。
该算法检测到 94.6%的修复体。分类消除了所有错误标记，最终，图像上标记了 90.5%的修复体。分类阶段区分真实修复体类别的整体准确率为 93.6%。
该机器学习算法在检测和分类全景图像上的牙体修复体方面表现出优异的性能。
