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

IF 3 2区 医学 Q3 ENGINEERING, BIOMEDICAL Annals of Biomedical Engineering Pub Date : 2024-06-17 DOI:10.1007/s10439-024-03559-0
Cristiana Adina Șalgău, Anca Morar, Andrei Daniel Zgarta, Diana-Larisa Ancuța, Alexandros Rădulescu, Ioan Liviu Mitrea, Andrei Ovidiu Tănase
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Abstract

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.

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机器学习在牙周病学和种植学中的应用:全面回顾
机器学习(ML)在牙科领域取得了重大进展,减轻了专业人员的工作量,提高了各种医疗流程的性能。牙周病学和种植学领域可以从这些进步中获益,例如确定牙周受损的牙齿、在种植规划过程中协助医生、确定种植体类型或预测种植体周围炎的发生。本论文概述了近期应用于牙周病学和种植学的 ML 技术,旨在确定不同医疗任务的流行模型,评估训练数据对自动算法成功的影响,并强调各种方法的优缺点。研究人员选取了 2016 年至 2023 年间发表的 48 篇原创研究论文,并将其分为四类:牙周病学、种植规划、种植体品牌和类型以及牙科种植体的成功率。从目的、技术细节、培训和测试数据的特点、结果和医学观察等方面对这些论文进行了分析。本文的目的不是提供一份详尽的调查报告,而是展示近期文献中具有代表性的方法,突出各种方法的优缺点,以及在牙科中应用机器学习的潜力。
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来源期刊
Annals of Biomedical Engineering
Annals of Biomedical Engineering 工程技术-工程:生物医学
CiteScore
7.50
自引率
15.80%
发文量
212
审稿时长
3 months
期刊介绍: Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.
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