J Yeungnam Med Sci.  2023 Nov;40(Suppl):S29-S36. 10.12701/jyms.2023.00465.

Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study

• Kong HJ

• KMID: 2548340
• DOI: http://doi.org/10.12701/jyms.2023.00465

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.
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.

Keyword

Artificial intelligence; Cloud computing; Convolutional neural networks; Deep learning; Dental implants

Figure

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  Fig. 1. Periapical radiographs and cropped images of the four types of selected implants. TSIII and USII: Osstem Implant Co. Ltd., Seoul, Korea; Osseotite External: Biomet 3i LLC, West Palm Beach, FL, USA; Xive S plus: Dentsply Sirona, York, PA, USA.

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  Fig. 2. Precision and recall curves on confidence threshold. (A) Precision-recall curve. It shows the trade-off between precision and recall. (B) Precision-recall by confidence threshold. It shows how model performs on the top-scored label along the full range of confidence threshold values.

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  Fig. 3. Confusion matrix for multiclass classification using Google AutoML Vision user interface. TSIII and USII: Osstem Implant Co. Ltd., Seoul, Korea; Osseotite External: Biomet 3i LLC, West Palm Beach, FL, USA; Xive S plus: Dentsply Sirona, York, PA, USA; Google AutoML Vision: Google LLC, Mountain View, CA, USA.

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  Fig. 4. Examples of false negatives and false positives. USII: Osstem Implant Co. Ltd., Seoul, Korea; Osseotite External: Biomet 3i LLC, West Palm Beach, FL, USA; Xive S plus: Dentsply Sirona, York, PA, USA.

Reference

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