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Artificial intelligence-driven automation of nasoalveolar molding device planning: A systematic review
Ahmed Yaseen Alqutaibi1, Hatem Hazzaa Hamadallah2, Muath Saad Alassaf3
1Associate Professor, Substitutive Dental Sciences Department (Prosthodontics), College of Dentistry, Taibah University, Al Madinah, Saudi Arabia; and Department of Prosthodontics, College of Dentistry, Ibb University, Ibb, Yemen.
Statement Of Problem:
Despite the increasing number of publications on applying artificial intelligence (AI) in the dental field, clarity regarding the performance of different approaches for nasoalveolar molding (NAM) planning and designing is lacking. Additionally, the overall robustness of the evidence in this field remains uncertain.
Purpose:
The purpose of this systematic review was to evaluate the role of AI in automating the prediction of anatomic landmarks and the design of NAM appliances.
Material And Methods:
A comprehensive literature search was conducted in major databases up to April 2024 without language restrictions. Studies applying AI algorithms for NAM landmark detection or appliance design were included. Data on study characteristics, AI methods, outcomes, and limitations were extracted.
Results:
Six studies met the eligibility criteria. AI algorithms demonstrated high accuracy in automatically detecting landmarks and designing NAM appliances. Approaches ranged from fully automated to semi-automated workflows. Most studies reported significant time savings compared with manual methods.
Conclusions:
AI applications in NAM demonstrate substantial potential in improving workflow design, as demonstrated by the high accuracy reported in various studies. The incorporation of AI in NAM planning leads to a significant reduction in treatment appointment times when compared with conventional manual methods, thereby potentially decreasing the overall duration of treatment. Nevertheless, additional research is required to foster better collaboration between dental professionals and AI experts, ultimately facilitating more efficient clinical integration.
