Volume 14, Issue 3 (9-2025)                   2025, 14(3): 15-23 | Back to browse issues page

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Daryoush S, Falahchai M, Hemmati Y B. Future AI Models for Predicting Orthodontic Treatment Duration: Pathways, Challenges, and Innovations. Journal title 2025; 14 (3) :15-23
URL: http://3dj.gums.ac.ir/article-1-657-en.html
Future AI Models for Predicting Orthodontic Treatment Duration: Pathways, Challenges, and Innovations
Shima Daryoush1 , Mehran Falahchai2 , Yasamin Babaee Hemmati *3
1- School of Dentistry, Guilan University of Medical Sciences, Rasht, Iran.
2- Department of Prosthodontics, Dental Sciences Research Center, School of Dentistry, Guilan University of Medical Sciences, Rasht, Iran.
3- Department of Orthodontics, Dental Sciences Research Center, School of Dentistry, Guilan University of Medical Sciences, Rasht, Iran. , Yasi.10482@gmail.com
Abstract:   (180 Views)
The duration of orthodontic treatment has always been a major concern for patients, and its estimation is challenging for dental clinicians. Recent relevant studies have shown that artificial intelligence (AI), especially machine learning (ML) and deep learning algorithms, can increase the accuracy of predictions compared to clinical estimates. However, there are still limitations, such as the use of single-center data, small sample size, lack of external validation, and bias towards the average value of the dataset. In addition, preoperative and intraoperative factors, including patient cooperation, biological characteristics, anomaly complexity, and treatment mechanics, affect the duration of orthodontic treatment. This narrative review highlights the importance of using multi-source data and external validation to enhance the generalizability and clinical trustworthiness of the models by reviewing the current evidence, examining methodological limitations, and suggesting future research directions, especially the development of hybrid, time-series, and adaptive models, and the application of explainable AI (XAI).
Keywords: Artificial Intelligence (AI), Orthodontics, Duration of Therapy, Deep Learning
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Type of Study: Original article | Subject: Radiology
Received: 2025/09/14 | Accepted: 2025/11/15 | Published: 2025/09/15
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