A comprehensive guide to develop algorithms using artificial intelligence in assessing, selecting, and predicting the suitable myofunctional appliance for Class II malocclusion
Rochana Mahesh, D. Gnaneswar, S. Suma, Sudarshan Patil Kulkarni, Ambati Akshaya, K. ShwethaBACKGROUND:
The application of AI in orthodontic treatment planning, particularly for myofunctional appliance therapy selection, represents a paradigmatic shift toward precision medicine.
AIMS AND OBJECTIVES:
This study investigates the potential of artificial intelligence (AI) driven tools to improve the diagnostic procedure, treatment planning, and prediction analysis for Class II malocclusion cases by utilizing developments in AI and machine learning (ML).
MATERIALS AND METHODS:
The methodology employed in this project integrates various ML techniques, including a multi layer perceptron (MLP), principal component analysis (PCA), and k nearest neighbors. Results: Four distinct methods were employed in this research; each aimed at enhancing the accuracy and efficiency of the algorithm.
CONCLUSION:
Among these methodologies, the MLP model emerged as the most successful, achieving an impressive accuracy rate of 99.17%. Among these methodologies, the MLP model emerged as the most successful, achieving an impressive accuracy rate of 99.17%.