Ismail Izzati Nabilah, Subramaniam Pram Kumar, Chi Adam Khairul Bariah, Ghazali Ahmad Badruddin
Oral and Maxillofacial Surgery Unit, Department of Oral and Maxillofacial Surgery and Oral Diagnosis, Kulliyyah of Dentistry, International Islamic University, Kuantan 25200, Malaysia.
Oral Radiology Unit, Department of Oral and Maxillofacial Surgery and Oral Diagnosis, Kulliyyah of Dentistry, International Islamic University, Kuantan 25200, Malaysia.
Diagnostics (Basel). 2024 Aug 30;14(17):1917. doi: 10.3390/diagnostics14171917.
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.
锥形束计算机断层扫描（CBCT）凭借其提供三维信息的能力、低辐射暴露、可承受性和广泛的可及性，已成为分析上气道的一种有前景的工具。人工智能（AI）在CBCT气道分析中的整合已显示出在诊断和管理气道相关病症的准确性和效率方面有所提高。本综述旨在探讨AI在CBCT气道分析中的当前应用，突出其组成部分和流程、应用、益处、挑战以及潜在的未来方向。进行了全面的文献综述，重点关注过去十年发表的讨论AI在CBCT气道分析中应用的研究。许多研究报告称，使用AI对CBCT气道体积进行分割和测量有显著改善，从而有助于准确诊断气道相关病症。此外，这些AI模型通过自动分割任务、体积测量和三维重建在气道分析应用中表现出高准确性和一致性，提高了诊断准确性并能预测治疗结果。尽管有这些进展，但将AI整合到临床工作流程中仍存在挑战。此外，不同人群和成像设置下AI性能的差异需要进一步的验证研究。持续的研发对于克服当前挑战并充分实现AI在气道分析中的潜力至关重要。
