关键词: 乳牙列, 牙弓大小, 牙弓形态, 错机制, 点云智能化分析

Abstract: Objective    To analyze the dental arch morphology in deciduous dentition malocclusion using an intelligent point cloud-based analysis system in order to investigate the impact of dental arch abnormalities on the development of deciduous dentition malocclusion. Methods    A total of 169 children aged 4 to 5 years in the stage of deciduous dentition，who voluntarily enrolled at the Second Kindergarten of Sichuan University between December 2022 and December 2023，were included in this study. Participants were divided into a malocclusion group（n = 104）and an individual normal occlusion group（n = 65）. The malocclusion group was further categorized into three subgroups based on malocclusion characteristics：the anterior deep overbite/overjet group（n = 16，exhibiting either deep overbite or deep overjet，or both），the dental crowding group（n = 39），and the anterior crossbite/reverse overjet group（n = 19，exhibiting either crossbite or reverse overjet，or both）. Other malocclusion types with smaller sample sizes were not analyzed. Intraoral scanning was performed to obtain three-dimensional models of the deciduous dental arch，and measurements of dental arch width and length were compared between the malocclusion and normal occlusion groups. An intelligent point cloud-based analysis system for dental arch morphology of deciduous dentition was developed and applied to assess the morphology and severity of abnormalities in the anterior deep overbite/overjet group. Results    ①The malocclusion group exhibited significantly smaller maxillary and mandibular intercanine arch widths，as well as maxillary inter-second molar arch length，compared to the normal occlusion group. However，maxillary and mandibular intercanine arch lengths were significantly greater in the malocclusion group than in the normal occlusion group（all P < 0.05）. In the anterior deep overbite/overjet group，both maxillary and mandibular intercanine arch widths were significantly smaller than those in the normal occlusion group，while maxillary and mandibular intercanine arch lengths，as well as mandibular inter-second molar arch width，were significantly greater. The dental crowding group showed significantly smaller maxillary and mandibular intercanine arch widths and mandibular inter-first molar arch length than the normal occlusion group，whereas the mandibular intercanine arch length was significantly greater in the dental crowding group. In the anterior crossbite/reverse overjet group，maxillary and mandibular intercanine arch widths were significantly smaller compared to those in the normal occlusion group，while maxillary and mandibular intercanine arch lengths were significantly greater（all P < 0.05）. ②The dental arch morphology in the normal occlusion group could be classified into seven clusters. The most common maxillary-mandibular arch pairing was maxillary type 3 paired with mandibular type 1（11.110%），followed by maxillary type 1 with mandibular type 4，maxillary type 3 with mandibular type 6，maxillary type 4 with mandibular type 7，maxillary type 6 with mandibular type 6，and maxillary type 7 with mandibular type 4（each 6.349%）. In the anterior deep overbite/overjet group，abnormal dental arch morphology could be categorized into four types：A（abnormalities in both maxillary and mandibular arches），B（abnormalities in the maxillary arch with a normal mandibular arch），C（abnormalities in the mandibular arch with a normal maxillary arch），and D（nearly normal maxillary and mandibular arches but with abnormal occlusal pairing）. Additionally，the severity of these dental arch abnormalities was quantified. Conclusion    Abnormalities in the size and morphology of the dental arch are present in deciduous dentition malocclusion. The application of an intelligent point cloud-based analysis system enables accurate diagnosis of dental arch abnormalities，demonstrating significant potential for early orthodontic diagnosis，treatment planning，and therapeutic outcome evaluation in pediatric patients.

Key words: deciduous dentition, arch size, arch morphology, malocclusion mechanism, intelligent point cloud-based analysis