Are multi-detector computed tomography and cone-beam computed tomography exams and software accurate to measure the upper airway? A systematic review

Abstract

Summary Background Cone-beam computed tomography (CBCT) has several applications in various fields of dental medicine such as diagnosis and treatment planning. When compared to computed tomography (CT), CBCT’s radiation exposure dose is decreased by 3%–20%. However, CBCT produces more scattered signals and may present poorer image quality when compared to medical CT. Objectives To review the findings regarding the accuracy of multi-detector computed tomography (MDCT) and CBCT and to compare the different software programs that segment the upper airway. Search methods Three databases (PubMed, Medline, and Web of Science) were searched for articles and a manual search was performed. Selection criteria The inclusion criteria were defined following the PICO framework: P—any patient with a CBCT or CT; I—dimensional evaluation of the upper airway using MDCT or CBCT; C—phantoms; O—the primary outcome was MDCT and CBCT accuracy, the secondary outcome was the evaluation and comparison of software programs used to segment the upper airway. Data collection and analysis Articles that met eligibility criteria were assessed using the Critical Appraisal Skills Program Checklist. Results Among the 16 eligible studies, 6 articles referred to the accuracy of MDCTs or CBCTs and 10 to the accuracy of the software. Most articles were qualified as high quality. Conclusions MDCT and CBCT scans’ accuracy in upper airway dimensional measurements depends on machine brand, parameters, and segmentation technique. Regarding the segmentation technique, 12 programs were studied. Most either underestimated or overestimated upper airway measurements. In particular, OnDemand3D and INVIVO showed poor accuracy. On the contrary, Invesalius, and MIMICS were accurate in assessing nasal cavities when using an interactive threshold. However, results varied due to methodological differences among the studies. Finally, fully automatic segmentation based on artificial intelligence may represent the future of airway segmentation because it is faster and seems to be accurate. However, further studies are necessary. Registration This study was registered in Prospero (International Prospective Register of Systematic Reviews) with the ID number CRD42022373998.