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Published on: September 8, 2023
Revealing the representative facial traits of different sagittal skeletal types: decipher what artificial
Jingyi Cai1, Yudi Deng1, Ziyang Min1
1State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China.
Objectives:
Aesthetic improvement is a significant concern in dental therapy. While orthodontic treatment primarily targets hard tissue, the impact on soft tissue and the extent of these changes remains empirical. This study aims to unveil the intricate relationship between facial soft tissue and skeletal types using artificial intelligence (AI) analysis.
Methods:
First, we collected a dataset of 1044 3-side-photographs and categorized them based on cephalometric measurements. After pre-processing and data augmentation, samples were fed to two independent models (Sfa, Res model) for training and testing. After validating that the Sfa model could accurately recognize the skeletal types based merely on photographs, Grad-CAM algorithm was utilized for model decipherment. Verification of the vital traits were carried out by facial adjustment simulation.
Results:
The Sfa model demonstrated superior accuracy (0.9293) in identifying skeletal types based solely on soft tissue, compared to the Res model (0.8395) and even trained orthodontists (0.764), testifying our hypothesis that AI could be more capable of processing imperceptible cues compared to mankind. Intriguingly, Grad-CAM revealed that cheek volume, forehead, chin and nasolabial traits could be representative features of each type, exceeding the traditional knowledge which merely concerns mandible and chin.
Conclusion:
By constructing a deep learning model as a classifier and then decipher it with Grad-CAM, we revealed the subtle and unnoticed cues associating skeletal and soft tissue, as well as provided a novel approach that could aid practitioners in devising tailored treatment plans for enhanced esthetic outcomes.
Clinical Significance:
The proposed AI methods offer valuable assistance to practitioners in identifying uncoordinated facial traits that may detract from a patient's attractiveness. By incorporating these insights into customized treatment plans, dental therapy can maximize esthetic benefits for individual patient.
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