目的:本系统综述和荟萃分析旨在评估基于人工智能(AI)的牙齿分割方法在三维锥形束计算机断层扫描(CBCT)图像中的当前性能,与手动分割技术相比,重点在于它们的准确性和效率。
方法:这篇综述中分析的数据包括利用AI算法在CBCT图像中进行牙齿分割的广泛研究。进行了Meta分析,重点使用骰子相似系数(DSC)对分割结果进行评价。
方法:PubMed,Embase,Scopus,WebofScience,和IEEEExplore进行了全面搜索,以确定相关研究。研究选择初始搜索产生5642个条目,随后的筛选和选择过程导致将35项研究纳入系统评价.在所采用的各种分割方法中,卷积神经网络,特别是U-net模型,是最常用的。DSC评分对牙齿分割的汇总效果为0.95(95CI0.94至0.96)。此外,七篇论文提供了对细分所需时间的见解,使用人工智能技术时,时间从1.5s到3.4min不等。
结论:AI模型在从CBCT图像自动分割牙齿方面表现出良好的准确性,同时减少了该过程所需的时间。然而,在未来的研究中,应解决使用不同成像方式对金属伪影和牙齿结构分割的矫正方法。
结论:AI算法在精确测量牙齿方面具有巨大潜力,正畸治疗计划,牙种植体放置,和其他需要精确牙齿轮廓的牙科手术。这些进步有助于改善牙科实践中的临床结果和患者护理。
This systematic
review and meta-analysis aimed to assess the current performance of artificial intelligence (AI)-based methods for tooth segmentation in three-dimensional cone-beam computed tomography (CBCT) images, with a focus on their accuracy and efficiency compared to those of manual segmentation techniques.
The data analyzed in this
review consisted of a wide range of research studies utilizing AI algorithms for tooth segmentation in CBCT images. Meta-analysis was performed, focusing on the evaluation of the segmentation results using the dice similarity coefficient (DSC).
PubMed, Embase, Scopus, Web of Science, and IEEE Explore were comprehensively searched to identify relevant studies. The initial search yielded 5642 entries, and subsequent screening and selection processes led to the inclusion of 35 studies in the systematic
review. Among the various segmentation methods employed, convolutional neural networks, particularly the U-net model, are the most commonly utilized. The pooled effect of the DSC score for tooth segmentation was 0.95 (95 %CI 0.94 to 0.96). Furthermore, seven papers provided insights into the time required for segmentation, which ranged from 1.5 s to 3.4 min when utilizing AI techniques.
AI models demonstrated favorable accuracy in automatically segmenting teeth from CBCT images while reducing the time required for the process. Nevertheless, correction methods for metal artifacts and tooth structure segmentation using different imaging modalities should be addressed in future studies.
AI algorithms have great potential for precise tooth measurements, orthodontic treatment planning, dental implant placement, and other dental procedures that require accurate tooth delineation. These advances have contributed to improved clinical outcomes and patient care in dental practice.