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@article{201479,
        author = {DR. NUPUR AGARWAL and Ashish Aggarwal and Nitin Upadhyay},
        title = {Artificial Intelligence in Airway Analysis Using Cone-Beam Computed Tomography: A Paradigm Shift in Dental Diagnostics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {4655-4657},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201479},
        abstract = {Airway morphology assessment is integral to diagnosing obstructive sleep apnea (OSA), planning orthodontic and orthognathic treatments, and evaluating craniofacial anomalies. Cone-Beam Computed Tomography (CBCT) has emerged as a preferred imaging modality in dentistry due to its high-resolution, three-dimensional visualization and reduced radiation exposure. However, manual segmentation and interpretation of airway structures remain labor-intensive and subject to inter-operator variability. Artificial Intelligence (AI), particularly deep learning algorithms, offers a transformative approach to automate and enhance CBCT-based airway analysis. This article reviews the current landscape of AI applications in airway evaluation using CBCT, highlighting its clinical utility, methodological advancements, and future directions.},
        keywords = {},
        month = {May},
        }