Department of Dental Material, Dental Branch, Islamic Azad University, Tehran, Iran.
Int Endod J. 2012 Mar;45(3):257-65. doi: 10.1111/j.1365-2591.2011.01970.x. Epub 2011 Oct 19.
To develop a new approach for locating the minor apical foramen (AF) using feature-extracting procedures from radiographs and then processing data using an artificial neural network (ANN) as a decision-making system.
Fifty straight single-rooted teeth were selected and placed in a socket within the alveolar bone of a dried skull. Access cavities were prepared and a file was place in the canals to determine the working length. A radiograph was taken to evaluate the location of the file in relation to the minor foramen and further checked after retrieving the tooth from the alveolar socket. The location of the file tip was categorized into: beyond the AF (long), within the root canal (short) and just at the minor AF (exact). Each radiograph was used to extract relevant features using K-means, Otsu method and Wavelet protocol. Thirty-six extracted features were used for training and the rest were used for evaluating the multi-layer Perceptron ANN model.
Analysis of the images from radiographs (test samples) by ANN showed that in 93% of the samples, the location of the AF had been determined correctly by false rejection and acceptation error methods.
Artificial neural networks can act as a second opinion to locate the AF on radiographs to enhance the accuracy of working length determination by radiography. In addition, ANN can function as a decision-making system in various similar clinical situations.
利用从射线照片中提取特征的程序开发一种新的方法来定位根尖小副孔（AF），然后使用人工神经网络（ANN）作为决策系统处理数据。
选择 50 颗直的单根牙并将其放置在干燥头骨牙槽骨内的牙槽中。制备进入腔并将锉放入根管中以确定工作长度。拍摄射线照片以评估锉相对于小副孔的位置，并在从牙槽中取出牙齿后再次检查。锉尖端的位置分为：超出 AF（长）、在根管内（短）和刚好在小 AF 处（精确）。每张射线照片都使用 K-means、Otsu 方法和小波协议提取相关特征。使用 36 个提取特征进行训练，其余用于评估多层感知器 ANN 模型。
通过 ANN 对射线照片（测试样本）中的图像进行分析表明，在 93%的样本中，AF 的位置已通过误判和接受误差方法正确确定。
人工神经网络可以作为一种辅助手段，通过射线照相定位 AF，以提高射线照相确定工作长度的准确性。此外，ANN 可以在各种类似的临床情况下作为决策系统发挥作用。
