Journal of Yeungnam Medical Science

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

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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;[Epub] 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

Review article

Trends in the study on medical education over the last 10 years, based on paper titles: Seong Yong Kim; Yeungnam Univ J Med. 2019;36(2):78-84. Published online May 13, 2019; DOI: https://doi.org/10.12701/yujm.2019.00206

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Abstract PDF

Medical education research subjects are incredibly diverse and have changed over time. This work in particular aims to compare and analyze research trends in medical education through the words used in the titles of these research papers. Academic Medicine (the journal of the Association of American Medical Colleges), Medical Teacher (the journal of the Association of Medical Education in Europe), the Korean Journal of Medical Education (KJME), and Korean Medical Education Review (KMER) were selected and analyzed for the purposes of this research. From 2009 to 2018, Academic Medicine and Medical Teacher published approximately 10 to 20 times more papers than the KJME and KMER. Frequently used words in these titles include “medical,” “student,” “education,” and “learning.” The words “clinical” and “learning” were used relatively often (7.80% to 13.66%) in Korean Journals and Medical Teacher, but Academic Medicine used these phrases relatively less often (6.47% and 4.41%, respectively). Concern with such various topics as problem-based learning, team-based learning, program evaluations, burnout, e-learning, and digital indicates that Medical Teacher seems to primarily deal with teaching and learning methodologies, and Academic Medicine handles all aspects of medical education. The KJME and KMER did not cover all subjects, as they publish smaller papers. However, it is anticipated that research on new subjects, such as artificial intelligence in medical education, will occur in the near future.

Citations

Citations to this article as recorded by

Assessing the effectiveness of massive open online courses on improving clinical skills in medical education in China: A meta-analysis
Ling Yang, Jiao Zou, Junwei Gao, Xiaotang Fan
Heliyon.2023; 9(8): e19263. CrossRef

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