A community detection analysis of malocclusion classes from orthodontics and upper airway data. (14th June 2021)

• Record Type: Journal Article
• Authors: Di Carlo, Gabriele; Gili, Tommaso; Caldarelli, Guido; Polimeni, Antonella; Cattaneo, Paolo M.
• Other Names: Yamashiro Takashi guestEditor.; Ko Ching‐Chang guestEditor.
• Is Part Of: Orthodontics and craniofacial research. Volume 24(2021)Supplement 2
• Page Start: 172
• Page End: 180
• Publication Date: 2021-06-14
• Subjects: malocclusions -- network analysis -- orthodontics -- upper airway; Skull -- Growth -- Periodicals; Facial bones -- Growth -- Periodicals; Orthodontics -- Periodicals; Orthodontics, Corrective -- Periodicals; Orthodontic appliances -- Periodicals; 617.51
• DOI: 10.1111/ocr.12490
• Languages: English
• ISSNs: 1601-6335

Abstract

Abstract: Objective: The interaction between skeletal class and upper airway has been extensively studied. Nevertheless, this relationship has not been clearly elucidated, with the heterogeneity of results suggesting the existence of different patterns for patients' classification, which has been elusive so far, probably due to oversimplified approaches. Hence, a network analysis was applied to test whether different patterns in patients' grouping exist. Settings and sample population: Ninety young adult patients with no obvious signs of respiratory diseases and no previous adeno‐tonsillectomy procedures, with thirty patients characterized as Class I (0 < ANB < 4); 30 Class II (ANB > 4); and 30 as Class III (ANB < 0). Materials and methods: A community detection approach was applied on a graph obtained from a previously analysed sample: thirty‐two measurements (nineteen cephalometric and thirteen upper airways data) were considered. Results: An airway‐orthodontic complex network has been obtained by cross‐correlating patients. Before entering the correlation, data were controlled for age and gender using linear regression and standardized. By including or not the upper airway measurements as independent variables, two different community structures were obtained. Each contained five modules, though with different patients' assignments. Conclusion: The community detection algorithm found the existence of more than the three classical skeletal classifications. These results support the development of alternative tools to classify subjects according to their craniofacial morphology. This approach could offer a powerful tool for implementing novel strategies for clinical and research in orthodontics.