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Original article
- Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study
- Hyun Jun Kong
- J Yeungnam Med Sci. 2023;40(Suppl):S29-S36. Published online July 26, 2023
- DOI: https://doi.org/10.12701/jyms.2023.00465
- 3,271 View
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- 4 Web of Science
- 6 Crossref
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Abstract
PDF - 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. -
Citations
Citations to this article as recorded by
- Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review
Wael I. Ibraheem
Diagnostics.2024; 14(8): 806. CrossRef - A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System
Mohammed A. H. Lubbad, Ikbal Leblebicioglu Kurtulus, Dervis Karaboga, Kerem Kilic, Alper Basturk, Bahriye Akay, Ozkan Ufuk Nalbantoglu, Ozden Melis Durmaz Yilmaz, Mustafa Ayata, Serkan Yilmaz, Ishak Pacal
Journal of Imaging Informatics in Medicine.2024; 37(5): 2559. CrossRef - Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review
Cristiana Adina Șalgău, Anca Morar, Andrei Daniel Zgarta, Diana-Larisa Ancuța, Alexandros Rădulescu, Ioan Liviu Mitrea, Andrei Ovidiu Tănase
Annals of Biomedical Engineering.2024; 52(9): 2348. CrossRef - Artificial neural networks development in prosthodontics - a systematic mapping review
Olivia Bobeică, Denis Iorga
Journal of Dentistry.2024; 151: 105385. CrossRef - Fracture strength of poly ether ether ketone abutment over short implant after fatigue
Mohamed A.E. Elsayed, Radwa A. El-dessouky, Mahmoud A.-A. Shakal
Tanta Dental Journal.2024; 21(3): 288. CrossRef - Advancements in artificial intelligence algorithms for dental implant identification: A systematic review with meta-analysis
Ahmed Yaseen Alqutaibi, Radhwan S. Algabri, Dina Elawady, Wafaa Ibrahim Ibrahim
The Journal of Prosthetic Dentistry.2023;[Epub] CrossRef
- Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review
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