PDF(Pubmed)
Abstract：
BACKGROUND: This study aimed to evaluate the accuracy and clinical usability of implant system classification using automated machine learning on a Google Cloud platform.
METHODS: Four dental implant systems were selected: Osstem TSIII, Osstem USII, Biomet 3i Os-seotite External, and Dentsply Sirona Xive. A total of 4,800 periapical radiographs (1,200 for each implant system) were collected and labeled based on electronic medical records. Regions of interest were manually cropped to 400×800 pixels, and all images were uploaded to Google Cloud storage. Approximately 80% of the images were used for training, 10% for validation, and 10% for testing. Google automated machine learning (AutoML) Vision automatically executed a neural architecture search technology to apply an appropriate algorithm to the uploaded data. A single-label image classification model was trained using AutoML. The performance of the mod-el was evaluated in terms of accuracy, precision, recall, specificity, and F1 score.
RESULTS: The accuracy, precision, recall, specificity, and F1 score of the AutoML Vision model were 0.981, 0.963, 0.961, 0.985, and 0.962, respectively. Osstem TSIII had an accuracy of 100%. Osstem USII and 3i Osseotite External were most often confused in the confusion matrix.
CONCLUSIONS: Deep learning-based AutoML on a cloud platform showed high accuracy in the classification of dental implant systems as a fine-tuned convolutional neural network. Higher-quality images from various implant systems will be required to improve the performance and clinical usability of the model.
METHODS: Four dental implant systems were selected: Osstem TSIII, Osstem USII, Biomet 3i Os-seotite External, and Dentsply Sirona Xive. A total of 4,800 periapical radiographs (1,200 for each implant system) were collected and labeled based on electronic medical records. Regions of interest were manually cropped to 400×800 pixels, and all images were uploaded to Google Cloud storage. Approximately 80% of the images were used for training, 10% for validation, and 10% for testing. Google automated machine learning (AutoML) Vision automatically executed a neural architecture search technology to apply an appropriate algorithm to the uploaded data. A single-label image classification model was trained using AutoML. The performance of the mod-el was evaluated in terms of accuracy, precision, recall, specificity, and F1 score.
RESULTS: The accuracy, precision, recall, specificity, and F1 score of the AutoML Vision model were 0.981, 0.963, 0.961, 0.985, and 0.962, respectively. Osstem TSIII had an accuracy of 100%. Osstem USII and 3i Osseotite External were most often confused in the confusion matrix.
CONCLUSIONS: Deep learning-based AutoML on a cloud platform showed high accuracy in the classification of dental implant systems as a fine-tuned convolutional neural network. Higher-quality images from various implant systems will be required to improve the performance and clinical usability of the model.
摘要：
■本研究旨在评估在GoogleCloud平台上使用自动机器学习进行植入物系统分类的准确性和临床可用性。
■选择了四种牙科种植系统：OsstemTSIII，OsstemUSII,Biomet3iOs-黑体外部，还有DentsplySironaXive.总共收集了4,800张根尖周X射线照片（每个植入物系统为1,200张），并根据电子病历进行了标记。感兴趣的区域被手动裁剪为400×800像素，所有图像都上传到谷歌云存储。大约80%的图像用于训练，10%用于验证，10%用于测试。Google自动机器学习（AutoML）Vision自动执行神经架构搜索技术，将适当的算法应用于上传的数据。使用AutoML训练单标签图像分类模型。在准确性方面评估了mod-el的性能，精度,召回，特异性，F1得分。
■准确性，精度,召回，特异性，AutoMLVision模型的F1评分分别为0.981、0.963、0.961、0.985和0.962。OssemTSIII的准确度为100％。OsstemUSII和3iOsseotiteExternal在混淆矩阵中最常被混淆。
■云平台上基于深度学习的AutoML在将牙种植体系统分类为微调卷积神经网络方面显示出很高的准确性。将需要来自各种植入系统的高质量图像，以提高模型的性能和临床可用性。
■选择了四种牙科种植系统：OsstemTSIII，OsstemUSII,Biomet3iOs-黑体外部，还有DentsplySironaXive.总共收集了4,800张根尖周X射线照片（每个植入物系统为1,200张），并根据电子病历进行了标记。感兴趣的区域被手动裁剪为400×800像素，所有图像都上传到谷歌云存储。大约80%的图像用于训练，10%用于验证，10%用于测试。Google自动机器学习（AutoML）Vision自动执行神经架构搜索技术，将适当的算法应用于上传的数据。使用AutoML训练单标签图像分类模型。在准确性方面评估了mod-el的性能，精度,召回，特异性，F1得分。
■准确性，精度,召回，特异性，AutoMLVision模型的F1评分分别为0.981、0.963、0.961、0.985和0.962。OssemTSIII的准确度为100％。OsstemUSII和3iOsseotiteExternal在混淆矩阵中最常被混淆。
■云平台上基于深度学习的AutoML在将牙种植体系统分类为微调卷积神经网络方面显示出很高的准确性。将需要来自各种植入系统的高质量图像，以提高模型的性能和临床可用性。
