In vitro Assessment of a Deep Learning-Based System for Computer-Aided Diagnosis and Treatment Planning in Orthodontics: A Virtual Study Using Digital Models.

INTRODUCTION

The use of artificial intelligence (AI) has the potential to improve both efficiency and accuracy in the process of automating orthodontic diagnosis and treatment planning operations. The purpose of this work is to evaluate the efficacy of a deep learning-based system for computer-aided diagnosis and treatment planning by using digital orthodontic models.

MATERIALS AND

METHODS

The AI system was trained to recognize malocclusions and generate treatment recommendations by using a collection of one hundred digital models in three dimensions. It was determined whether the results generated by AI were comparable with the evaluations made by qualified orthodontists.

RESULTS

The technique obtained a high level of accuracy in malocclusion categorization, with sensitivity and specificity that were both greater than 90% in the majority of cases.

CONCLUSION

Despite the fact that these findings lend credence to the potential of AI in orthodontic operations, more clinical validation requires further investigation.