An In-Depth Analysis of the Predictive Value of Nasolabial Angle on Nasal and Dental Morphology: Utilizing Machine Learning Techniques for Enhanced Orthodontic Assessment

Abstract

Background: Protrusive facial abnormalities, characterized by the anterior positioning of the lips relative to the facial profile, are prevalent malocclusions that significantly impact dental and facial aesthetics. These conditions can result from complex interactions among dental, skeletal, and soft tissue components, necessitating a comprehensive diagnostic approach. Methods: This review systematically evaluates the predictive value of the nasolabial angle on nasal and dental morphology, utilizing machine learning techniques. A literature search was conducted in databases such as PubMed and Scopus, focusing on studies that assess the relationship between nasolabial angle measurements and the corresponding morphological features of the nasal and dental structures. Results: The analysis of selected studies demonstrates that variations in the nasolabial angle are predictive of underlying nasal and dental morphologies. Machine learning models have been applied successfully to identify patterns and correlations, offering improved diagnostic accuracy and treatment planning for orthodontic interventions. However, the review also highlights the need for standardized metrics in assessing the nasolabial angle to enhance the generalizability of findings across diverse populations. Conclusion: The nasolabial angle serves as a crucial indicator of both nasal and dental morphology, with machine learning techniques providing valuable insights into its predictive capabilities. This underscores the importance of integrating advanced analytical methods in orthodontic assessments to optimize treatment outcomes for patients with protrusive facial abnormalities. Further research is recommended to establish uniform definitions and methodologies for measuring the nasolabial angle in clinical practice.