Global Research Trends and Focuses on Pierre Robin Sequence From 1992 to 2023: A Bibliometric Analysis of the Past 3 Decades.

There is currently a lack of scientific bibliometric analyses in the field of Pierre Robin sequence (PRS). Pierre Robin sequence is known for its clinical triad of micrognathia, glossoptosis, airway obstruction, and possible secondary cleft palate. These defects can lead to upper airway obstruction, sleep apnea, feeding difficulties, affect an individual's growth and development, education level, and in severe cases can be life-threatening. Through analysis of literature retrieved from the Web of Science Core Collection (WoSCC) database using Results Analysis and Citation Report and Citespace software, 933 original articles and reviews were included after manual screening. The overall trend for the number of annual publications and citations was increasing. On the basis of the analysis, airway evaluation and treatment, mandibular distraction osteogenesis (MDO), as well as descriptions of PRS characteristics have been the focus of research in this field. In addition, with advances in new technologies such as gene sequencing and expanding understanding of diseases among researchers, research on genetics and etiology related to PRS has become a growing trend.

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning.

Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation. The features extracted using RSSN and DSN were fused to optimize the quality of boundary regression, which provided impressive results for multiple evaluation metrics. Specifically, the proposed framework achieved a top F1 score of 0.9849, a top Dice metric score of 0.9629, and an mAP (IOU = 0.5) score of 0.9810. This framework holds great promise for enhancing the clinical efficiency of dentists in tooth segmentation and numbering tasks.