What question did this study set out to answer?

The study aims to explore the use of a hybrid AI model to improve the diagnosis and treatment of malocclusion.

March 14, 2026Open Access

The Role of Generative Artificial Intelligence in the Diagnosis and Treatment of Malocclusion

NANajah A AlsayedHamad Medical Corporation

Key Points

The study aims to explore the use of a hybrid AI model to improve the diagnosis and treatment of malocclusion.
Developed a hybrid AI model using CNNs, GANs, and LLMs for analyzing orthodontic images.
Used a dataset of 150 anonymized expert-labeled lateral cephalometric images.
Employed data preprocessing, augmentation, and feature extraction techniques.
Evaluated model performance through classification accuracy and confidence scores.
Achieved high accuracy in predicting malocclusion classes with confidence scores over 85%.
Demonstrated improved generalization from limited data through synthetic image augmentation.
Enhanced clinical data interpretation and treatment recommendations using LLM integration.

Abstract

Background Malocclusion is a prevalent dental disorder characterized by the improper alignment of the teeth and jaws, which adversely affects oral function and aesthetics. Traditional diagnosis and treatment planning procedures are time-consuming and rely heavily on expert evaluation. Recent developments in generative artificial intelligence (AI) offer promising tools to enhance diagnostic accuracy and to optimize treatment strategies in orthodontics. Methods This study developed a hybrid artificial AI model combining convolutional neural networks (CNNs), generative adversarial Networks (GANs), and large language models (LLMs), specifically, large language model Meta AI (LLaMA), to analyze orthodontic images and predict malocclusion types. A dataset comprising 150 anonymized, expert-labeled lateral cephalometric images from Hamad Dental Center in Doha was divided into training and testing sets using an 80:20 split. Data preprocessing, augmentation, and feature extraction were employed to improve model robustness. The model’s performance was evaluated through classification accuracy and confidence scores. Results The hybrid model demonstrated high accuracy in predicting malocclusion classes, achieving confidence scores exceeding 85% across multiple test cases. Synthetic image augmentation via GAN improved the model’s ability to generalize from limited data. The integration of LLM facilitated enhanced interpretation of clinical data, supporting precise treatment recommendations. Conclusion The generative AI-driven hybrid model effectively supports the diagnosis and treatment planning for malocclusion, thereby offering a valuable tool for orthodontic practice. Its ability to learn from limited data and provide high-confidence predictions streamlines clinical workflows. Future work will focus on expanding datasets, improving model explainability, and conducting clinical validation to ensure broader adoption in precision orthodontics.

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