Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised te...

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Bibliographic Details
Published in:Annals of biomedical engineering 2024-09, Vol.52 (9), p.2348-2371
Main Authors: Șalgău, Cristiana Adina, Morar, Anca, Zgarta, Andrei Daniel, Ancuța, Diana-Larisa, Rădulescu, Alexandros, Mitrea, Ioan Liviu, Tănase, Andrei Ovidiu
Format: Article
Language:English
Subjects:
Algorithms
Biochemistry
Biological and Medical Physics
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Biophysics
Classical Mechanics
Dental Implantation - methods
Dental Implants
Dental prosthetics
Dentistry
Humans
Learning algorithms
Machine Learning
Observational learning
Periodontics
Review
Surgical implants
Teeth
Training
Transplants & implants
Working conditions
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Summary:Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.
ISSN:0090-6964
1573-9686
1573-9686
DOI:10.1007/s10439-024-03559-0