Accuracy and Time Efficiency of Automated Tooth Segmentation in Dental Imaging-A Systematic Review and Meta-Analysis.

IF 2.4 3区 医学 Q2 DENTISTRY, ORAL SURGERY & MEDICINE
Neeraj Kumar Dudy, Shubhnita Verma, Prasad Chitra
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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.

牙齿成像中自动牙齿分割的准确性和时效性——系统综述和荟萃分析。
与人工或地面真值技术相比,本系统综述检查了基于人工智能的自动牙齿分割方法的准确性和效率。全面检索MEDLINE(通过PubMed)、Cochrane中央对照试验注册、ScienceDirect、SciELO、LILACS、德国国家医学图书馆、IEEE explore、Web of Science和灰色文献来源(OpenGrey),检索截止至2024年1月1日,无任何限制。使用诊断准确性研究质量评估工具-2 (QUADAS-2)评估纳入研究的偏倚风险。42项研究纳入系统评价,其中37项纳入meta分析。灵敏度为0.75 ~ 1,特异度为0.85 ~ 1,具有较好的分割精度。人工智能与人工方法的骰子分割系数比较无显著差异(SMD = 0.05, p = 0.9),而基础真值人工智能算法优于人工智能算法(SMD = 2.42, p
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来源期刊
Orthodontics & Craniofacial Research
Orthodontics & Craniofacial Research 医学-牙科与口腔外科
CiteScore
5.30
自引率
3.20%
发文量
65
审稿时长
>12 weeks
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.
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