Comparisons of artificial intelligence automated segmentation techniques to manual segmentation techniques of the maxilla and maxillary sinus for CT or cone-beam CT scans-a systematic review.

OBJECTIVES

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, artificial intelligence (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.

METHODS

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.

RESULTS

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 cone-beam computed tomography (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.

CONCLUSIONS

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.