Comparative Evaluation of 3D Imaging Techniques in Soft Tissue Evaluation Using Machine Learning

Rashmi R, Tulika Tripathi

Abstract: Three-dimensional (3D) imaging has revolutionized soft tissue evaluation in orthodontics, enabling accurate, volumetric assessment beyond the limitations of traditional two-dimensional techniques. When integrated with artificial intelligence (AI) and machine learning (ML), these modalities facilitate automated landmark detection, precise morphometric analysis, treatment outcome prediction, and dynamic functional assessment. This narrative review examines 3D imaging techniques including cone-beam computed tomography, stereophotogrammetry, structured light scanning, laser scanning, ultrasonography, and magnetic resonance imaging highlighting their principles, advantages, limitations, and clinical applications. Comparative analysis reveals stereophotogrammetry as the gold standard for soft tissue capture, with AI enhancing its predictive and analytical capabilities. Despite significant advancements, challenges such as data standardization, computational demands, and ethical considerations persist. Future directions emphasize protocol harmonization, multimodal integration, and real-time AI-assisted diagnostics. The convergence of 3D imaging and AI promises to advance precision orthodontics, optimize treatment planning, and improve patient-centered outcomes.

Keywords: 3D imaging, orthodontics, artificial intelligence, machine learning, stereophotogrammetry

How to Cite?: Rashmi R, Tulika Tripathi, "Comparative Evaluation of 3D Imaging Techniques in Soft Tissue Evaluation Using Machine Learning", Volume 14 Issue 8, August 2025, International Journal of Science and Research (IJSR), Pages: 978-984, https://www.ijsr.net/getabstract.php?paperid=SR25819133947, DOI: https://dx.doi.org/10.21275/SR25819133947