Mahmood Dashti, Jimmy Londono, Shohreh Ghasemi, Niusha Zare, Meyassara Samman, Heba Ashi, Mohammad Hosein Amirzade-Iranaq, Farshad Khosraviani, Mohammad Sabeti, Zohaib Khurshid
{"title":"Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review.","authors":"Mahmood Dashti, Jimmy Londono, Shohreh Ghasemi, Niusha Zare, Meyassara Samman, Heba Ashi, Mohammad Hosein Amirzade-Iranaq, Farshad Khosraviani, Mohammad Sabeti, Zohaib Khurshid","doi":"10.7717/peerj-cs.2371","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>In recent years, artificial intelligence (AI) and deep learning (DL) have made a considerable impact in dentistry, specifically in advancing image processing algorithms for detecting caries from radiographical images. Despite this progress, there is still a lack of data on the effectiveness of these algorithms in accurately identifying caries. This study provides an overview aimed at evaluating and comparing reviews that focus on the detection of <i>dental caries (DC)</i> using DL algorithms from 2D radiographs.</p><p><strong>Materials and methods: </strong>This comprehensive umbrella review adhered to the \"Reporting guideline for overviews of reviews of healthcare interventions\" (PRIOR). Specific keywords were generated to assess the accuracy of AI and DL algorithms in detecting DC from radiographical images. To ensure the highest quality of research, thorough searches were performed on PubMed/Medline, Web of Science, Scopus, and Embase. Additionally, bias in the selected articles was rigorously assessed using the Joanna Briggs Institute (JBI) tool.</p><p><strong>Results: </strong>In this umbrella review, seven systematic reviews (SRs) were assessed from a total of 77 studies included. Various DL algorithms were used across these studies, with conventional neural networks and other techniques being the predominant methods for detecting DC. The SRs included in the study examined 24 original articles that used 2D radiographical images for caries detection. Accuracy rates varied between 0.733 and 0.986 across datasets ranging in size from 15 to 2,500 images.</p><p><strong>Conclusion: </strong>The advancement of DL algorithms in detecting and predicting DC through radiographic imaging is a significant breakthrough. These algorithms excel in extracting subtle features from radiographic images and applying machine learning techniques to achieve highly accurate predictions, often outperforming human experts. This advancement holds immense potential to transform diagnostic processes in dentistry, promising to considerably improve patient outcomes.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2371"},"PeriodicalIF":3.5000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622875/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PeerJ Computer Science","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.7717/peerj-cs.2371","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Background: In recent years, artificial intelligence (AI) and deep learning (DL) have made a considerable impact in dentistry, specifically in advancing image processing algorithms for detecting caries from radiographical images. Despite this progress, there is still a lack of data on the effectiveness of these algorithms in accurately identifying caries. This study provides an overview aimed at evaluating and comparing reviews that focus on the detection of dental caries (DC) using DL algorithms from 2D radiographs.
Materials and methods: This comprehensive umbrella review adhered to the "Reporting guideline for overviews of reviews of healthcare interventions" (PRIOR). Specific keywords were generated to assess the accuracy of AI and DL algorithms in detecting DC from radiographical images. To ensure the highest quality of research, thorough searches were performed on PubMed/Medline, Web of Science, Scopus, and Embase. Additionally, bias in the selected articles was rigorously assessed using the Joanna Briggs Institute (JBI) tool.
Results: In this umbrella review, seven systematic reviews (SRs) were assessed from a total of 77 studies included. Various DL algorithms were used across these studies, with conventional neural networks and other techniques being the predominant methods for detecting DC. The SRs included in the study examined 24 original articles that used 2D radiographical images for caries detection. Accuracy rates varied between 0.733 and 0.986 across datasets ranging in size from 15 to 2,500 images.
Conclusion: The advancement of DL algorithms in detecting and predicting DC through radiographic imaging is a significant breakthrough. These algorithms excel in extracting subtle features from radiographic images and applying machine learning techniques to achieve highly accurate predictions, often outperforming human experts. This advancement holds immense potential to transform diagnostic processes in dentistry, promising to considerably improve patient outcomes.
背景:近年来,人工智能(AI)和深度学习(DL)在牙科领域产生了相当大的影响,特别是在改进图像处理算法以从放射图像中检测龋齿方面。尽管取得了这些进展,但这些算法在准确识别龋齿方面的有效性仍然缺乏数据。本研究提供了一个概述,旨在评估和比较的评论,重点是利用二维x线片的DL算法检测龋齿(DC)。材料和方法:本综合综述遵循“卫生保健干预措施综述报告指南”(PRIOR)。生成了特定的关键词来评估AI和DL算法在从放射图像中检测DC时的准确性。为了确保研究的最高质量,我们在PubMed/Medline、Web of Science、Scopus和Embase上进行了全面的搜索。此外,使用乔安娜布里格斯研究所(JBI)工具严格评估所选文章的偏倚。结果:在这一总括性综述中,共纳入77项研究,评估了7项系统评价(SRs)。在这些研究中使用了各种深度学习算法,传统的神经网络和其他技术是检测深度学习的主要方法。研究中包括的SRs检查了24篇使用二维放射图像检测龋齿的原始文章。在15到2500张图像的数据集上,准确率在0.733到0.986之间变化。结论:通过x线影像检测和预测DC的DL算法的进步是一项重大突破。这些算法擅长于从放射图像中提取细微特征,并应用机器学习技术实现高度准确的预测,通常优于人类专家。这一进步在改变牙科诊断过程方面具有巨大的潜力,有望大大改善患者的治疗效果。
期刊介绍:
PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.