Efficiency and accuracy of artificial intelligence in the radiographic detection of periodontal bone loss: A systematic review.

IF 1.7 Q3 DENTISTRY, ORAL SURGERY & MEDICINE Imaging Science in Dentistry Pub Date : 2023-09-01 Epub Date: 2023-08-02 DOI:10.5624/isd.20230092
Asmhan Tariq, Fatmah Bin Nakhi, Fatema Salah, Gabass Eltayeb, Ghada Jassem Abdulla, Noor Najim, Salma Ahmed Khedr, Sara Elkerdasy, Natheer Al-Rawi, Sausan Alkawas, Marwan Mohammed, Shishir Ram Shetty
{"title":"Efficiency and accuracy of artificial intelligence in the radiographic detection of periodontal bone loss: A systematic review.","authors":"Asmhan Tariq,&nbsp;Fatmah Bin Nakhi,&nbsp;Fatema Salah,&nbsp;Gabass Eltayeb,&nbsp;Ghada Jassem Abdulla,&nbsp;Noor Najim,&nbsp;Salma Ahmed Khedr,&nbsp;Sara Elkerdasy,&nbsp;Natheer Al-Rawi,&nbsp;Sausan Alkawas,&nbsp;Marwan Mohammed,&nbsp;Shishir Ram Shetty","doi":"10.5624/isd.20230092","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Artificial intelligence (AI) is poised to play a major role in medical diagnostics. Periodontal disease is one of the most common oral diseases. The early diagnosis of periodontal disease is essential for effective treatment and a favorable prognosis. This study aimed to assess the effectiveness of AI in diagnosing periodontal bone loss through radiographic analysis.</p><p><strong>Materials and methods: </strong>A literature search involving 5 databases (PubMed, ScienceDirect, Scopus, Health and Medical Collection, Dentistry and Oral Sciences) was carried out. A specific combination of keywords was used to obtain the articles. The PRISMA guidelines were used to filter eligible articles. The study design, sample size, type of AI software, and the results of each eligible study were analyzed. The CASP diagnostic study checklist was used to evaluate the evidence strength score.</p><p><strong>Results: </strong>Seven articles were eligible for review according to the PRISMA guidelines. Out of the 7 eligible studies, 4 had strong CASP evidence strength scores (7-8/9). The remaining studies had intermediate CASP evidence strength scores (3.5-6.5/9). The highest area under the curve among the reported studies was 94%, the highest F1 score was 91%, and the highest specificity and sensitivity were 98.1% and 94%, respectively.</p><p><strong>Conclusion: </strong>AI-based detection of periodontal bone loss using radiographs is an efficient method. However, more clinical studies need to be conducted before this method is introduced into routine dental practice.</p>","PeriodicalId":51714,"journal":{"name":"Imaging Science in Dentistry","volume":"53 3","pages":"193-198"},"PeriodicalIF":1.7000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/22/fe/isd-53-193.PMC10548158.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Imaging Science in Dentistry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5624/isd.20230092","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/2 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose: Artificial intelligence (AI) is poised to play a major role in medical diagnostics. Periodontal disease is one of the most common oral diseases. The early diagnosis of periodontal disease is essential for effective treatment and a favorable prognosis. This study aimed to assess the effectiveness of AI in diagnosing periodontal bone loss through radiographic analysis.

Materials and methods: A literature search involving 5 databases (PubMed, ScienceDirect, Scopus, Health and Medical Collection, Dentistry and Oral Sciences) was carried out. A specific combination of keywords was used to obtain the articles. The PRISMA guidelines were used to filter eligible articles. The study design, sample size, type of AI software, and the results of each eligible study were analyzed. The CASP diagnostic study checklist was used to evaluate the evidence strength score.

Results: Seven articles were eligible for review according to the PRISMA guidelines. Out of the 7 eligible studies, 4 had strong CASP evidence strength scores (7-8/9). The remaining studies had intermediate CASP evidence strength scores (3.5-6.5/9). The highest area under the curve among the reported studies was 94%, the highest F1 score was 91%, and the highest specificity and sensitivity were 98.1% and 94%, respectively.

Conclusion: AI-based detection of periodontal bone loss using radiographs is an efficient method. However, more clinical studies need to be conducted before this method is introduced into routine dental practice.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
人工智能在牙周骨丢失放射学检测中的效率和准确性:一项系统综述。
目的:人工智能(AI)将在医学诊断中发挥重要作用。牙周病是最常见的口腔疾病之一。牙周病的早期诊断对于有效的治疗和良好的预后至关重要。本研究旨在通过放射学分析评估人工智能在诊断牙周骨丢失方面的有效性。材料和方法:文献检索涉及5个数据库(PubMed、ScienceDirect、Scopus、Health and Medical Collection、Dentistry and Oral Sciences)。使用特定的关键字组合来获得这些文章。PRISMA指南用于筛选符合条件的文章。对研究设计、样本量、人工智能软件类型以及每项合格研究的结果进行了分析。CASP诊断研究检查表用于评估证据强度评分。结果:根据PRISMA指南,有7篇文章符合审查条件。在7项符合条件的研究中,4项具有较强的CASP证据强度得分(7-8/9)。其余研究的CASP证据强度得分为中等(3.5-6.5/9)。报告研究中曲线下面积最高为94%,F1得分最高为91%,特异性和敏感性最高分别为98.1%和94%。结论:基于AI的牙周骨丢失X线片检测是一种有效的方法。然而,在将这种方法引入常规牙科实践之前,还需要进行更多的临床研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Imaging Science in Dentistry
Imaging Science in Dentistry DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
2.90
自引率
11.10%
发文量
42
期刊最新文献
Classification of mandibular molar furcation involvement in periapical radiographs by deep learning. Clinical validity and precision of deep learning-based cone-beam computed tomography automatic landmarking algorithm. Combination of metal artifact reduction and sharpening filter application for horizontal root fracture diagnosis in teeth adjacent to a zirconia implant. Erratum to: McCune-Albright syndrome with acromegaly: A case report with characteristic radiographic features of fibrous dysplasia. Evaluation of deep learning and convolutional neural network algorithms for mandibular fracture detection using radiographic images: A systematic review and meta-analysis.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1