基于云的深度学习算法的牙种植体系统分类实验研究。

IF 1 Q3 MEDICINE, GENERAL & INTERNAL
Journal of Yeungnam medical science Pub Date : 2023-11-01 Epub Date: 2023-07-26 DOI:10.12701/jyms.2023.00465
Hyun Jun Kong
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引用次数: 0

摘要

背景:本研究旨在评估在谷歌云平台上使用自动机器学习进行种植体系统分类的准确性和临床可用性。方法:选择4种种植体系统:Osstem TSIII、Osstem USII、Biomet 3i Os-seotite External和Dentsply Sirona Xive。收集了4800张根尖周x线片(每个种植体系统1200张),并根据电子病历进行标记。感兴趣的区域被手动裁剪为400×800像素,所有图像都被上传到谷歌云存储。大约80%的图像用于训练,10%用于验证,10%用于测试。谷歌自动机器学习(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。模型的性能从准确性、精密度、召回率、特异性和F1评分方面进行评估。结果:AutoML Vision模型的准确率、精密度、召回率、特异性和F1评分分别为0.981、0.963、0.961、0.985和0.962。系统TSIII的准确度为100%。在混淆矩阵中,系统USII和3i骨梯外部最容易混淆。结论:基于云平台的深度学习AutoML作为一种微调卷积神经网络,在牙种植体系统分类中具有较高的准确率。将需要来自各种植入系统的高质量图像来提高模型的性能和临床可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study.

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.

Conclusion: 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.

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