Cone Beam Computed Tomography: Craniofacial and Airway Analysis

Application of Artificial Intelligence in Cone-Beam Computed Tomography for Airway Analysis: A Narrative Review

Cone-beam computed tomography (CBCT) has emerged as a promising tool for the analysis of the upper airway, leveraging on its ability to provide three-dimensional information, minimal radiation exposure, affordability, and widespread accessibility. The integration of artificial intelligence (AI) in CBCT for airway analysis has shown improvements in the accuracy and efficiency of diagnosing and managing airway-related conditions. This review aims to explore the current applications of AI in CBCT for airway analysis, highlighting its components and processes, applications, benefits, challenges, and potential future directions. A comprehensive literature review was conducted, focusing on studies published in the last decade that discuss AI applications in CBCT airway analysis. Many studies reported the significant improvement in segmentation and measurement of airway volumes from CBCT using AI, thereby facilitating accurate diagnosis of airway-related conditions. In addition, these AI models demonstrated high accuracy and consistency in their application for airway analysis through automated segmentation tasks, volume measurement, and 3D reconstruction, which enhanced the diagnostic accuracy and allowed predictive treatment outcomes. Despite these advancements, challenges remain in the integration of AI into clinical workflows. Furthermore, variability in AI performance across different populations and imaging settings necessitates further validation studies. Continued research and development are essential to overcome current challenges and fully realize the potential of AI in airway analysis.

The Reliability of Three-Dimensional Landmark-Based Craniomaxillofacial and Airway Cephalometric Analysis

Cephalometric analysis is a standard diagnostic tool in orthodontics and craniofacial surgery. Today, as conventional 2D cephalometry is limited and susceptible to analysis bias, a more reliable and user-friendly three-dimensional system that includes hard tissue, soft tissue, and airways is demanded in clinical practice. We launched our study to develop such a system based on CT data and landmarks. This study aims to determine whether the data labeled through our process is highly qualified and whether the soft tissue and airway data derived from CT scans are reliable. We enrolled 15 patients (seven males, eight females, 26.47 ± 3.44 years old) diagnosed with either non-syndromic dento-maxillofacial deformities or OSDB in this study to evaluate the intra- and inter-examiner reliability of our system. A total of 126 landmarks were adopted and divided into five sets by region: 28 cranial points, 25 mandibular points, 20 teeth points, 48 soft tissue points, and 6 airway points. All the landmarks were labeled by two experienced clinical practitioners, either of whom had labeled all the data twice at least one month apart. Furthermore, 78 parameters of three sets were calculated in this study: 42 skeletal parameters (23 angular and 19 linear), 27 soft tissue parameters (9 angular and 18 linear), and 9 upper airway parameters (2 linear, 4 areal, and 3 voluminal). Intraclass correlation coefficient (ICC) was used to evaluate the inter-examiner and intra-examiner reliability of landmark coordinate values and measurement parameters. The overwhelming majority of the landmarks showed excellent intra- and inter-examiner reliability. For skeletal parameters, angular parameters indicated better reliability, while linear parameters performed better for soft tissue parameters. The intra- and inter-examiner ICCs of airway parameters referred to excellent reliability. In summary, the data labeled through our process are qualified, and the soft tissue and airway data derived from CT scans are reliable. Landmarks that are not commonly used in clinical practice may require additional attention while labeling as they are prone to poor reliability. Measurement parameters with values close to 0 tend to have low reliability. We believe this three-dimensional cephalometric system would reach clinical application.

Evaluation of the oropharynx in class I and II skeletal patterns by CBCT

PURPOSE

This study aimed to evaluate the dimensions of the oropharynx and its shape in the minimum cross-sectional area, in individuals with Class I and Class II skeletal patterns, using three-dimensional CBCT images.

METHODS

Forty-two cone-beam computed tomography images of grown individuals were evaluated. The images were divided according to the patient's skeletal patterns. The dimensions of the oropharyngeal airway space were determined using the Dolphin Imaging software.

RESULTS

The volume and the minimum cross-sectional area were greater in patients with a Class I skeletal pattern, with a median difference of 5379 mm³ and 86.8 mm², respectively. The anteroposterior and lateral diameters in the minimum cross-sectional area were also higher in Class I individuals (2.3 and 6.0 mm, respectively), but the ratio between them was not different.

CONCLUSIONS

The volume and the minimum cross-sectional area of the oropharynx, as well as the anteroposterior and lateral diameters, are lower in individuals with a class II skeletal pattern than in individuals with a class I skeletal pattern. There was no difference in the shape of the oropharynx in healthy individuals with different skeletal patterns.

Three-dimensional segmentation of the upper airway using cone beam CT: a systematic review

The objectives of this study were to systematically review the literature for studies that used cone beam CT (CBCT) to automatically or semi-automatically model the upper airway (including the pharyngeal, nasal and paranasal airways), and to assess their validity and reliability. Several electronic databases (MEDLINE®, MEDLINE In-Process & Other Non-Indexed Citations, all evidence-based medicine reviews including the Cochrane database, and Scopus) were searched. Abstracts that appeared to meet the initial selection criteria were selected by consensus. The original articles were then retrieved and their references were searched manually for potentially suitable articles that were missed during the electronic search. Final articles that met all the selection criteria were evaluated using a customized evaluation checklist. 16 articles were finally selected. From these, five scored more than 50% based on their methodology. Although eight articles reported the reliability of the airway model generated, only three used intraclass correlation (ICC). Two articles tested the accuracy/validity of airway models against the gold standard, manual segmentation, using volumetric measurements; however, neither used ICC. Only three articles properly tested the reliability of the three-dimensional (3D) upper airway model generated from CBCT and only one article had sufficiently sound methodology to test the airway model's accuracy/validity. The literature lacks proper scientific justification of a solid and optimized CBCT protocol for airway imaging. Owing to the limited number of adequate studies, it is difficult to generate a strong conclusion regarding the current validity and reliability of CBCT-generated 3D models.