There have been few studies on congenital nasal dermoid and sinus cysts (NDSCs) in infants. This study was performed to obtain clinical data for the diagnosis and treatment of NDSCs in infants. We performed a retrospective analysis of 11 infants admitted with NDSCs between 2014 and 2019. Patient demographics, lesion site, preoperative radiological findings, surgical technique, intraoperative findings, and postoperative sequelae were analyzed. In total, 11 infants (average age, 19 months; lowest age, 10 months) were included in this study. All patients presented with a nasal root mass, 2 patients also had nasal tip fistula, and only 1 case had a history of preoperative infection. Preoperative enhanced computed tomography (CT) examination showed nasal surface lesion (type I) in 3, nasal intraosseous (type II) in 5, intracranial epidural (type III) in 2, and intracranial dural (type IV) in 1 patient. The main surgical methods included direct resection with a vertical midline incision (9 patients), vertical incision + transnasal endoscopic resection + skull base repair (1 patient), and transverse incision of the lower margin of the left eyebrow (1 patient). All wounds healed well without serious complications. Using the 4-type classification method in combination with the preoperative CT findings to analyze the extent of NDSC in infants is helpful for formulating the surgical plan. Using vertical incision approach alone or combined with nasal endoscopy for minimally invasive surgery can meet the needs of complete resection and reconstruction. Our results provide clinical data that can help establish standardized criteria for the diagnosis and treatment of NDSCs in infants.

The growing demand for building information modeling (BIM) data and ubiquitous applications make it increasingly necessary to establish a reliable way to share the models on lightweight devices. Building scenes have strong occlusion features and the building exterior plays an important role in digital devices with limited computational resources. This allows the possibility to reduce the resource consumption while roaming in outdoor scenes by culling away the interior building data. This article addresses the task of automatic annotation of BIM building exterior via voxel index analysis. We showcase the research of using industry foundation classes (IFC) and other mainstream formats as our input data and proposed an automatic algorithm for annotating the building exterior. Afterward, a practical and accurate voxel index analysis procedure is designed for frequently flawed models. The annotation can be added directly into the original data file under the same IFC standard, avoiding the complex procedure and information loss in semantics mapping between different standards. The final examinations show the robustness of our algorithm and the capability of handling large BIM building models.