Journal of Yeungnam Medical Science

Original articles

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

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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 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

Comparison of the removal torque and a histomorphometric evaluation of the RBM treated implants with the RBM followed by laser treated implants: an experimental study in rabbits: Eun Young Park, Hae Ok Sohn, Eun-Kyong Kim; Yeungnam Univ J Med. 2019;36(1):43-49. Published online January 11, 2019; DOI: https://doi.org/10.12701/yujm.2019.00094

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Abstract PDF

Background
In the osseointegration of dental implants, the implant surface properties have been reported to be some of the most important critical factors. The effect of implant’s surfaces created by resorbable blast media (RBM) followed by laser ablation on bone tissue reactions was examined using the removal torque test and histomorphometric analysis.
Methods
Two types of dental implants, RBM-laser implants (experimental group) and RBM implants (control group) (CSM implant system, Daegu, Korea; L=6 mm, diameter=3.75 mm) were placed into the right and left distal femoral metaphysis of 17 adult rabbits. Six weeks after placement, removal torque was measured and histomorphometric analysis was performed.
Results
The mean removal torque was 24.0±10.2 Ncm and 46.6±16.4 Ncm for the control and test specimens, respectively. The experimental RBM-laser implants had significantly higher removal torque values than the control RBM implants (p=0.013). The mean values of total and cortical bone to implant contact (BIC) were respectively 46.3±10.8% and 65.3±12.5% for the experimental group, and 41.9±18.5% and 57.6±10.6% for the control group. The experimental RBM-laser implants showed a higher degree of total and cortical BIC compared with RBM implants, but there was no statistical significance (p=0.482, 0.225).
Conclusion
The removal torque and BIC of the test group were higher than those of the control group. In this study, the surface treatment created by RBM treatment followed by laser ablation appears to have a potential in improving bone tissue reactions of dental implants.

Citations

Citations to this article as recorded by

Determining primary stability for adhesively stabilized dental implants
Ole Zoffmann Andersen, Benjamin Bellón, Maryam Lamkaouchi, Marzia Brunelli, Qiuju Wei, Philip Procter, Benjamin E. Pippenger
Clinical Oral Investigations.2023; 27(7): 3741. CrossRef

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