Twin block therapy in a child with class II malocclusion and mouth breathing assisted by AI-driven software for cephalometric analysis: case report

Claudia Nur Rizky Jayanti; Risti Saptarini Primarti

doi:10.24198/pjd.vol37no1.59299

Twin block therapy in a child with class II malocclusion and mouth breathing assisted by AI-driven software for cephalometric analysis: case report

Claudia Nur Rizky Jayanti, Risti Saptarini Primarti

Abstract

Introduction: The Twin Block appliance is widely recognized as an effective treatment for Class II malocclusion in growing children. While cephalometric analysis software, including AI-based tools, is a well-established diagnostic aid, its integration into treatment planning enhances diagnostic precision and progress monitoring. This case report aims to highlight the management of a child with Class II malocclusion and mouth breathing using a Twin Block appliance, assisted by AI-driven software for cephalometric analysis to enhance treatment outcomes. Case Report: A 10-year-old boy presented with forward-positioned teeth and low self-confidence. Clinical examination revealed Class II malocclusion characterized by upper incisor protrusion and mandibular retrusion. The water-retention test confirmed mouth breathing. AI-driven cephalometric analysis using WebCeph showed an SNB angle of 75.59° and an ANB angle of 6.77°. The patient underwent nine months of Twin Block therapy with monthly adjustments. Conclusion: Twin Block therapy in this case was ineffective in achieving skeletal growth modification, likely due to factors such as treatment timing, skeletal maturity, or appliance design. AI-driven software for cephalometric analysis proved valuable for monitoring but should be viewed as a diagnostic tool rather than a determinant of treatment success.

Keywords

AI-driven cephalometry, children, class II malocclusion, mouth breathing, twin block

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DOI: https://doi.org/10.24198/pjd.vol37no1.59299