Application of Artificial Intelligence in Cone-Beam Computed Tomography for Airway Analysis: A Narrative Review
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Published:2024-08-30
Issue:17
Volume:14
Page:1917
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ISSN:2075-4418
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Container-title:Diagnostics
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language:en
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Short-container-title:Diagnostics
Author:
Ismail Izzati Nabilah1ORCID, Subramaniam Pram Kumar1ORCID, Chi Adam Khairul Bariah1, Ghazali Ahmad Badruddin2ORCID
Affiliation:
1. Oral and Maxillofacial Surgery Unit, Department of Oral and Maxillofacial Surgery and Oral Diagnosis, Kulliyyah of Dentistry, International Islamic University, Kuantan 25200, Malaysia 2. Oral Radiology Unit, Department of Oral and Maxillofacial Surgery and Oral Diagnosis, Kulliyyah of Dentistry, International Islamic University, Kuantan 25200, Malaysia
Abstract
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.
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