Park Joon Ha, Hamimi Mustafa, Choi Joanne Jung Eun, Figueredo Carlos Marcelo S, Cameron Mr Andrew
School of Medicine and Dentistry, Griffith University, Gold Coast, Australia.
Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago.
Dentomaxillofac Radiol. 2025 Jun 3. doi: 10.1093/dmfr/twaf042.
Accurate segmentation of the maxillary sinus from medical images is essential for diagnostic purposes and surgical planning. Manual segmentation of the maxillary sinus, while the gold standard, is time consuming and requires adequate training. To overcome this problem, AI enabled automatic segmentation software's developed. The purpose of this review is to systematically analyse the current literature to investigate the accuracy and efficiency of automatic segmentation techniques of the maxillary sinus to manual segmentation.
A systematic approach to perform a thorough analysis of the existing literature using PRISMA guidelines. Data for this study was obtained from Pubmed, Medline, Embase, and Google Scholar databases. The inclusion and exclusion eligibility criteria were used to shortlist relevant studies. The sample size, anatomical structures segmented, experience of operators, type of manual segmentation software used, type of automatic segmentation software used, statistical comparative method used, and length of time of segmentation were analysed.
This systematic review presents 10 studies that compared the accuracy and efficiency of automatic segmentation of the maxillary sinus to manual segmentation. All the studies included in this study were found to have a low risk of bias. Samples sizes ranged from 3 to 144, a variety of operators were used to manually segment the CBCT and segmentation was made primarily to 3D slicer and Mimics software. The comparison was primarily made to Unet architecture softwares, with the dice-coefficient being the primary means of comparison.
This systematic review showed that automatic segmentation technique was consistently faster than manual segmentation techniques and over 90% accurate when compared to the gold standard of manual segmentation.
从医学图像中准确分割出上颌窦对于诊断目的和手术规划至关重要。上颌窦的手动分割虽是金标准，但耗时且需要充分培训。为克服这一问题，已开发出人工智能驱动的自动分割软件。本综述的目的是系统分析当前文献，以研究上颌窦自动分割技术相对于手动分割的准确性和效率。
采用系统方法，依据PRISMA指南对现有文献进行全面分析。本研究的数据来自PubMed、Medline、Embase和谷歌学术数据库。使用纳入和排除资格标准筛选相关研究。分析了样本量、分割的解剖结构、操作者经验、使用的手动分割软件类型、使用的自动分割软件类型、使用的统计比较方法以及分割时间长度。
本系统综述呈现了10项比较上颌窦自动分割与手动分割的准确性和效率的研究。本研究纳入的所有研究均被发现存在低偏倚风险。样本量从3到144不等，使用了多种操作者对手动分割CBCT，且分割主要针对3D Slicer和Mimics软件。比较主要针对Unet架构软件，骰子系数是主要比较手段。
本系统综述表明，自动分割技术始终比手动分割技术更快，与手动分割的金标准相比，准确率超过90%。
