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KMID : 1812320230400000029
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Journal of Yeungnam Medical Science
2023 Volume.40 No. 0 p.29 ~ p.36
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Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study
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Kong Hyun-Jun
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Abstract
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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 Osseotite 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 model 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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KEYWORD
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Artificial intelligence, Cloud computing, Computer neural networks, Deep learning, Dental implants
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