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Cephalometric Evaluation of Mouth-Breathing Patients: A Retrospective Study Using Sassouni, Down, and Steiner
Bhawana Kumari1, Rajeev K Singh1, Richa Khanna1
1Paediatric and Preventive Dentistry, King George's Medical University, Lucknow, IND.
Abstract:
Background Mouth breathing is a prevalent condition in children and is often associated with altered craniofacial development. Cephalometric analysis is a key diagnostic tool used to evaluate these changes. However, different cephalometric analyses may yield inconsistent diagnostic outcomes, potentially affecting treatment decisions. This study aimed to evaluate and compare skeletal anteroposterior relationships, maxillary and mandibular positions, and growth direction in mouth-breathing children using Sassouni, Down, and Steiner analyses. Methodology This retrospective, cross-sectional study included 88 lateral cephalometric radiographs of mouth-breathing children aged 6-14 years. Cephalograms were analyzed independently using the Sassouni, Down, and Steiner methods. Parameters assessed included skeletal classification, maxillary and mandibular positions, and facial growth direction. Statistical analyses were performed using paired t-tests, chi-square tests, and kappa statistics to evaluate inter-method variability and significance. Results Significant differences were found among the three analyses in all evaluated parameters. The Sassouni method identified more Class II skeletal patterns and normal maxillary positions compared to the Steiner method, which showed higher rates of retrognathic jaws. Down's analysis indicated a greater proportion of normal growth direction than Sassouni's. All differences were statistically significant (p < 0.05), highlighting substantial diagnostic variability. Conclusions These findings underscore the diagnostic inconsistency among commonly used cephalometric analyses in assessing mouth-breathing children. These variations have important clinical implications, emphasizing the need for a comprehensive diagnostic approach that integrates multiple analyses and clinical assessments to guide accurate treatment planning.
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