Iranian
Biomedical Journal
Volume 28, Issue 7 (Special Issue 2024)
IBJ 2024, 28(7): 363-363 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Nikbakht M H, Amani Beni P, Shirban F. Using Artificial Intelligence and Machine Learning to Predict the Success of Dental Implant Treatment: A Systematic Review. IBJ 2024; 28 (7) :363-363
URL: http://ibj.pasteur.ac.ir/article-1-4800-en.html
URL: http://ibj.pasteur.ac.ir/article-1-4800-en.html
Abstract:
Introduction: Nowadays, dental implants are widely used to replace missing teeth. However, complications, failures, and diseases are not uncommon. Accurate prediction of implant success is influenced by many factors, making it a challenging subject for dentists. One of the contributions of machine learning (ML) to the medical field is the prediction of treatment outcomes. We aimed to review studies using ML and artificial intelligence (AI) to predict dental implant treatment success.
Search Strategy: Our systematic review focused on the literature regarding methods for predicting the success of AI-assisted implant treatments. A search was conducted in five databases: MEDLINE/PubMed, EMBASE, Web of Science, Cochrane, and Scopus, using Keywords “artificial intelligence”, “machine learning”, “dental implants”, “prognosis”, and “prediction”. A manual search was conducted, as well. Studies that investigated the clinical applications of AI to predict implant success using patient risk factors were included. Review articles, letters to the editors, and posters were excluded. Relevant articles published up to October 2022 were identified and assessed for quality, and data was extracted by two reviewers.
Results: A duplication check was conducted, and 74 articles were identified. After screening and applying inclusion and exclusion criteria, seven studies were reviewed that investigated the success of implant treatment using AI methods. Different ML models and input data were used in the studies. Among the included studies, the accuracy, sensitivity, and specificity of these methods in all eligible studies ranged from 62.40% (for LR) to 99.25% (for NN), 48.08% (for K-NN) to 97.63% (for NN), and 61.11% (for DT) to 100% (for SVM), respectively.
Conclusion and Discussion: According to the findings, the NN method showed the highest accuracy and sensitivity, and the highest specificity belonged to SVM. They also reported that dental implant prognosis mostly depends on factors such as the mesiodistal position of the inserted implant, fixture width, and implant system.
Search Strategy: Our systematic review focused on the literature regarding methods for predicting the success of AI-assisted implant treatments. A search was conducted in five databases: MEDLINE/PubMed, EMBASE, Web of Science, Cochrane, and Scopus, using Keywords “artificial intelligence”, “machine learning”, “dental implants”, “prognosis”, and “prediction”. A manual search was conducted, as well. Studies that investigated the clinical applications of AI to predict implant success using patient risk factors were included. Review articles, letters to the editors, and posters were excluded. Relevant articles published up to October 2022 were identified and assessed for quality, and data was extracted by two reviewers.
Results: A duplication check was conducted, and 74 articles were identified. After screening and applying inclusion and exclusion criteria, seven studies were reviewed that investigated the success of implant treatment using AI methods. Different ML models and input data were used in the studies. Among the included studies, the accuracy, sensitivity, and specificity of these methods in all eligible studies ranged from 62.40% (for LR) to 99.25% (for NN), 48.08% (for K-NN) to 97.63% (for NN), and 61.11% (for DT) to 100% (for SVM), respectively.
Conclusion and Discussion: According to the findings, the NN method showed the highest accuracy and sensitivity, and the highest specificity belonged to SVM. They also reported that dental implant prognosis mostly depends on factors such as the mesiodistal position of the inserted implant, fixture width, and implant system.
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
IBJ is a member of Committee on Publication Ethics (COPE)
and follows its guidelines |
The articles of this journal are licensed under
a Creative Commons Attribution-NonDerivatives 4.0 International License. .png) |
 IBJ is owned and published by Pasteur Institute of Iran |