Comparison of Soft Tissue Cephalometric Parameters Distinguishing Skeletal Class I, II and III Malocclusion

Habib, Maria; Ahsan, Tabassum; Majeed, Omair; Jawaid, Mazia

doi:10.25301/jpda.291.14

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…They found the highest NLA for Class I malocclusion. This is also in partial agreement with the study of Bhardwaj et al [ 28 ] and Habib et al [ 29 ]. They too found the highest NLA for Class I followed by Class II and least for Class III.…”

Section: Discussionsupporting

confidence: 93%

A Retrospective Study Comparing Nose, Lip, and Chin Morphology in Class I, Class II, and Class III Skeletal Relationships in Patients Visiting to the Department of Orthodontics, BPKIHS: A Cephalometric Study

Chaudhary

Giri

Gyawali

et al. 2022

International Journal of Dentistry

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Background. With the changing paradigm from primarily hard tissue to soft tissue evaluation for orthodontic diagnosis and treatment planning, the priority has shifted to bring about improvement in the profile and smile characteristics of patients. Since not only the esthetics but also the stability of orthodontic treatment is largely determined by the soft tissue envelope, proper positioning of the soft tissue drape becomes paramount. Soft tissues of face, namely, nose, lips, and chin, are of paramount importance not only from an esthetic but also from functional and treatment stability points. Objective. To determine the morphological variation of lips, nose, and chin in different skeletal malocclusions. Materials and Methods. Lateral cephalograms of 237 patients visiting the department of orthodontics, BPKIHS, were taken, hand traced on matt acetate tracing paper of 0.002″ thickness with 0.3-mm 2B pencil. Samples were divided into 3 skeletal classes based on ANB angle. Measurements were made in relation to the nose, lips, and chin. Data were inserted in to SPSS and analyzed statistically using descriptive statistics, and mean and standard deviation was calculated for each variable. Multiple comparison between groups was done with post hoc Bonferroni test with mean difference significant at p < 0.05 . Result. On intergroup comparison, a significant difference was found for upper lip thickness (ULT) between Class II and Class III, and lower lip length (LLL) between Class I and Class III, and between Class II and Class III. Significant difference for nasolabial angle (NLA) was found between Class II and Class III. Similarly, a significant difference for the vertical chin parameter (CTV) was found between Class I and Class III, and between Class II and Class III. Conclusion. Both upper and lower lip thicknesses were highest for Class III followed by Class I and Class II, respectively. Lip lengths too were found to be highest for Class III skeletal relation. Nasolabial angle was larger in Class II malocclusion when compared to Class I and Class III. Similarly, both nasal length and nasal height measurements were in the order of Class III > Class II > Class I. Both horizontal and vertical chin parameters were larger for Class III sagittal relation.

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Section: Discussionsupporting

confidence: 93%

A Retrospective Study Comparing Nose, Lip, and Chin Morphology in Class I, Class II, and Class III Skeletal Relationships in Patients Visiting to the Department of Orthodontics, BPKIHS: A Cephalometric Study

Chaudhary

Giri

Gyawali

et al. 2022

International Journal of Dentistry

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The concept of AI-assisted self-monitoring for skeletal malocclusion

Zhang,

Liu,

Yang

et al. 2024

Health Informatics J

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Background: Skeletal malocclusion is common among populations. Its severity often increases during adolescence, yet it is frequently overlooked. The introduction of deep learning in stomatology has opened a new avenue for self-health management. Methods: In this study, networks were trained using lateral photographs of 2109 newly diagnosed patients. The performance of the models was thoroughly evaluated using various metrics, such as sensitivity, specificity, accuracy, confusion matrix analysis, the receiver operating characteristic curve, and the area under the curve value. Heat maps were generated to further interpret the models' decisions. A comparative analysis was performed to assess the proposed models against the expert judgment of orthodontic specialists. Results: The modified models reached an impressive average accuracy of 84.50% (78.73%-88.87%), with both sex and developmental stage information contributing to the AI system’s enhanced performance. The heat maps effectively highlighted the distinct characteristics of skeletal class II and III malocclusion in specific regions. In contrast, the specialist achieved a mean accuracy of 71.89% (65.25%-77.64%). Conclusions: Deep learning appears to be a promising tool for assisting in the screening of skeletal malocclusion. It provides valuable insights for expanding the use of AI in self-monitoring and early detection within a family environment.

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Comparison of Soft Tissue Cephalometric Parameters Distinguishing Skeletal Class I, II and III Malocclusion

Cited by 2 publications

References 7 publications

A Retrospective Study Comparing Nose, Lip, and Chin Morphology in Class I, Class II, and Class III Skeletal Relationships in Patients Visiting to the Department of Orthodontics, BPKIHS: A Cephalometric Study

A Retrospective Study Comparing Nose, Lip, and Chin Morphology in Class I, Class II, and Class III Skeletal Relationships in Patients Visiting to the Department of Orthodontics, BPKIHS: A Cephalometric Study

The concept of AI-assisted self-monitoring for skeletal malocclusion

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