[An Improved Object Detection Algorithm for Thyroid Nodule Ultrasound Image Based on Faster R-CNN]

Objective

To propose an improved algorithm for thyroid nodule object detection based on Faster R-CNN so as to improve the detection precision of thyroid nodules in ultrasound images.

Methods

The algorithm used ResNeSt50 combined with deformable convolution (DC) as the backbone network to improve the detection effect of irregularly shaped nodules. Feature pyramid networks (FPN) and Region of Interest (RoI) Align were introduced in the back of the trunk network. The former was used to reduce missed or mistaken detection of thyroid nodules, and the latter was used to improve the detection precision of small nodules. To improve the generalization ability of the model, parameters were updated during backpropagation with an optimizer improved by Sharpness-Aware Minimization (SAM).

Results

In this experiment, 6 261 thyroid ultrasound images from the Affiliated Hospital of Xuzhou Medical University and the First Hospital of Nanjing were used to compare and evaluate the effectiveness of the improved algorithm. According to the findings, the algorithm showed optimization effect to a certain degree, with the AP50 of the final test set being as high as 97.4% and AP@50:5:95 also showing a 10.0% improvement compared with the original model. Compared with both the original model and the existing models, the improved algorithm had higher detection precision and improved capacity to detect thyroid nodules with better accuracy and precision. In particular, the improved algorithm had a higher recall rate under the requirement of lower detection frame precision.

Conclusion

The improved method proposed in the study is an effective object detection algorithm for thyroid nodules and can be used to detect thyroid nodules with accuracy and precision.

[Research Updates: Relationship between Gingival Epithelial Intercellular Junctions and Periodontal Pathogenic Bacteria]

Gingival epithelial barrier is the first line of defense of periodontal tissues against the invasion of pathogenic bacteria. The destruction of gingival epithelial barrier is closely related to the development of periodontal disease. Studies have shown that periodontal pathogenic bacteria and their inflammatory microenvironment can inhibit the expression of gingival epithelial junctional proteins via molecular mechanisms such as the downregulation of the expression of grainyhead-like protein family and the upregulation of the methylation level of gene promoter of epithelial connexin, and thus cause damage to the gingival epithelial barrier and the development of periodontitis. We herein reviewed the effects of bacteria and inflammatory factors induced by bacterial infection on gingival epithelial intercellular junctions and related mechanisms, and summarized the research progress on the relationship between gingival epithelial intercellular junctions and periodontal pathogenic bacteria in recent years. Most recent studies were focused on i n vitro cytological experiments and animal models of infections caused by a single kind of bacterium. We have suggested that building gingival epithelial organoid model and combining multi-omics approaches with high resolution three-dimensional electron microscopy are expected to help pinpoint the key microorganisms and their most important virulence factors that trigger periodontal microecologcal imbalance and cause functional damage to the gingival epithelial barrier, to reveal the key molecular mechanisms involved in the maintenance and destruction of gingival epithelial barrier function, and to provide new perspectives on the pathogenesis and the clinical prevention and treatment of periodontitis.