How AI‐Driven Root and Bone Predictions Can Assist Clear Aligner Treatment Planning

拥挤 放射治疗计划 可预测性 计算机科学 人气 还原(数学) 牙周组织 人工智能 牙科 医学 心理学 内科学 神经科学 物理 社会心理学 量子力学 放射治疗 数学 几何学
作者
Eser Tüfekçi,Caroline K. Carrico,Christina B. Gordon,Steven J. Lindauer
出处
期刊:Orthodontics & Craniofacial Research [Wiley]
标识
DOI:10.1111/ocr.12921
摘要

ABSTRACT Integrating artificial intelligence (AI) and advanced three‐dimensional (3D) imaging has revolutionised dentistry by enhancing diagnostics and treatment planning. Advanced algorithms and machine‐learning techniques may enable orthodontists to analyse complex cases and predict treatment outcomes accurately. This technology facilitates the creation of customised treatment plans that consider individual tooth morphology and periodontal health, optimising force application and minimising treatment time. Since their introduction, clear aligners have gained popularity, with over 17 million people treated by 2023. Compared with fixed appliances, clear aligners offer advantages, such as better aesthetics, comfort and oral hygiene. Treating patients with a compromised periodontium requires accurate diagnosis and treatment planning. This paper reviews how AI‐driven treatment planning software predicting root movement and visualising bone structures may impact treatment decisions and, ultimately, treatment outcomes. The technology behind machine learning and AI in designing clear aligners is discussed. Research shows that when viewing the cases in 3D, clinicians are more comfortable when treating crowding cases with a non‐extraction approach using interproximal reduction (IPR) only. However, it was interesting to note that clinicians with extensive experience treating clear aligner patients were more comfortable using IPR to address severe crowding cases when viewed in 2D, compared with those less experienced with clear aligners. However, when the cases were visualised in 3D, both groups showed equal comfort in using IPR, as the roots were within the bone. AI‐driven treatment planning software, utilising machine learning in conjunction with 3D modelling, may enhance the predictability of orthodontic movements while reducing treatment time and increasing patient satisfaction.

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