{"title":"Accuracy and Time Efficiency of Automated Tooth Segmentation in Dental Imaging-A Systematic Review and Meta-Analysis.","authors":"Neeraj Kumar Dudy, Shubhnita Verma, Prasad Chitra","doi":"10.1111/ocr.12924","DOIUrl":null,"url":null,"abstract":"<p><p>This systematic review examined the accuracy and efficiency of AI-based automated tooth segmentation methods compared to manual or ground truth techniques. A comprehensive search was conducted in MEDLINE (via PubMed), the Cochrane Central Register of Controlled Trials, ScienceDirect, SciELO, LILACS, the German National Library of Medicine, IEEE Xplore, Web of Science and grey literature sources (OpenGrey) up to 1 January 2024, without restrictions. The Quality Assessment Tool for Diagnostic Accuracy Studies-2 (QUADAS-2) was used to evaluate the risk of bias in the included studies. Forty-two studies were included in the systematic review, of which 37 were included in the meta-analysis. Sensitivity and specificity values ranged from 0.75 to 1 and 0.85 to 1, respectively, indicating good segmentation accuracy. Comparisons of the dice segmentation coefficient between AI and manual methods showed no significant difference (SMD = 0.05, p = 0.9), whereas ground truth AI algorithms outperformed proposed AI algorithms (SMD = 2.42, p < 0.00001). The Hausdorff distance revealed no significant difference between AI and manual methods, but proposed AI algorithms demonstrated superiority over ground truth AI algorithms (SMD = -5.76, p < 0.01). AI algorithms were also significantly faster than manual methods. Current evidence suggests that AI algorithms for tooth segmentation perform comparably to manual segmentation. Moreover, recent automated algorithms have shown superior performance compared to ground truth algorithms. However, these findings should be interpreted cautiously due to the very low certainty of evidence, largely attributed to bias and high heterogeneity. Further well-designed and rigorously reported studies are necessary.</p>","PeriodicalId":19652,"journal":{"name":"Orthodontics & Craniofacial Research","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Orthodontics & Craniofacial Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/ocr.12924","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
引用次数: 0
Abstract
This systematic review examined the accuracy and efficiency of AI-based automated tooth segmentation methods compared to manual or ground truth techniques. A comprehensive search was conducted in MEDLINE (via PubMed), the Cochrane Central Register of Controlled Trials, ScienceDirect, SciELO, LILACS, the German National Library of Medicine, IEEE Xplore, Web of Science and grey literature sources (OpenGrey) up to 1 January 2024, without restrictions. The Quality Assessment Tool for Diagnostic Accuracy Studies-2 (QUADAS-2) was used to evaluate the risk of bias in the included studies. Forty-two studies were included in the systematic review, of which 37 were included in the meta-analysis. Sensitivity and specificity values ranged from 0.75 to 1 and 0.85 to 1, respectively, indicating good segmentation accuracy. Comparisons of the dice segmentation coefficient between AI and manual methods showed no significant difference (SMD = 0.05, p = 0.9), whereas ground truth AI algorithms outperformed proposed AI algorithms (SMD = 2.42, p < 0.00001). The Hausdorff distance revealed no significant difference between AI and manual methods, but proposed AI algorithms demonstrated superiority over ground truth AI algorithms (SMD = -5.76, p < 0.01). AI algorithms were also significantly faster than manual methods. Current evidence suggests that AI algorithms for tooth segmentation perform comparably to manual segmentation. Moreover, recent automated algorithms have shown superior performance compared to ground truth algorithms. However, these findings should be interpreted cautiously due to the very low certainty of evidence, largely attributed to bias and high heterogeneity. Further well-designed and rigorously reported studies are necessary.
期刊介绍:
Orthodontics & Craniofacial Research - Genes, Growth and Development is published to serve its readers as an international forum for the presentation and critical discussion of issues pertinent to the advancement of the specialty of orthodontics and the evidence-based knowledge of craniofacial growth and development. This forum is based on scientifically supported information, but also includes minority and conflicting opinions.
The objective of the journal is to facilitate effective communication between the research community and practicing clinicians. Original papers of high scientific quality that report the findings of clinical trials, clinical epidemiology, and novel therapeutic or diagnostic approaches are appropriate submissions. Similarly, we welcome papers in genetics, developmental biology, syndromology, surgery, speech and hearing, and other biomedical disciplines related to clinical orthodontics and normal and abnormal craniofacial growth and development. In addition to original and basic research, the journal publishes concise reviews, case reports of substantial value, invited essays, letters, and announcements.
The journal is published quarterly. The review of submitted papers will be coordinated by the editor and members of the editorial board. It is policy to review manuscripts within 3 to 4 weeks of receipt and to publish within 3 to 6 months of acceptance.