The prediction of satisfactory orthodontic treatment outcomes can be very challenging owing to the subjectivity of orthodontists’ judgment, along with the inherent difficulties when considering numerous factors. Therefore, this study introduced a deep learning-based method for predicting orthodontic treatment outcomes based on the image-to-image translation of dental radiographs using the Pix2Pix model. This proposed method addresses the aforementioned issues using a Pix2Pix-based prediction model constructed from adversarial deep learning. Patient datasets and prediction models were separated and developed for extraction and non-extraction treatments, respectively. The patients’ radiographs were pre-processed and standardized for training, testing, and applying the Pix2Pix models by uniformly adjusting the degree of blackness for the region of interest. A comparison of actual with Pix2Pix-predicted images revealed high accuracy, with correlation coefficients of 0.8767 for extraction orthodontic treatments and 0.8686 for non-extraction treatments. The proposed method establishes a robust clinical and practical framework for digital dentistry, offering both quantitative and qualitative insights for orthodontists and patients.

Introduction
Materials and Methods
    1. Cases collection: radiological image dataset
    2. Pre-process of datasets for deep-learning model
    3. Deep learning model - Pix2Pix model
    4. Pairing the dataset for adversarial learning
    5. Training, validation, and application
Results
Discussion
Funding
Acknowledgements
Conflicts of Interest
References

• Kyunghwa Baek(Department of Pharmacology, College of Dentistry, Research Institute of Oral Science, Gangneung-Wonju National University, Gangneung 25457, Republic of Korea)
• Jihye Jang(Department of Pharmacology, College of Dentistry, Research Institute of Oral Science, Gangneung-Wonju National University, Gangneung 25457, Republic of Korea)
• Seong-Hee Ko(Department of Pharmacology, College of Dentistry, Research Institute of Oral Science, Gangneung-Wonju National University, Gangneung 25457, Republic of Korea)
• Yerin Kim(Department of Environment and Energy, Sejong University, Seoul 05006, Republic of Korea, Space Situational Awareness Research Team, Korea Aerospace Research Institute, Daejeon 34133, Republic of Korea)
• Sungwook Hong(Department of Environment and Energy, Sejong University, Seoul 05006, Republic of Korea, DeepThoTh Co., Ltd., Seoul 05006, Republic of Korea) Corresponding author
• Insan Jang(Department of Orthodontics, Gangneung-Wonju National University, Gangneung 25457, Republic of Korea)
• Dong-Soon Choi(Department of Orthodontics, Gangneung-Wonju National University, Gangneung 25457, Republic of Korea)