Sample Size Estimation for Developing Artificial Intelligence to Predict Orthodontic Treatment Outcomes
10.5856/JKDS.2025.18.1.12
- Author:
Jong-Hak KIM
1
;
Naeun KWON
;
Shin-Jae LEE
Author Information
1. Department of Orthodontics, Seoul National University, Seoul, Korea
- Publication Type:ORIGINAL ARTICLE
- From:Journal of Korean Dental Science
2025;18(1):12-19
- CountryRepublic of Korea
- Language:English
-
Abstract:
Purpose:To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment.
Materials and Methods:From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size.
Results:The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area.
Conclusion:From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.
