Clinical Application of Primary Suture Following Three-Port Laparoscopic Common Bile Duct Exploration: A Report of 176 Cases

Objective: To investigate the feasibility, safety and the clinical value of primary suture following 3-port laparoscopic common bile duct exploration (LCBDE). Methods: From January 2012 to September 2014, 176 patients suffered from choledocholithiasis were treated with primary suture following 3-port LCBDE and the clinical data were retrospectively analyzed. Results: All cases were operated successfully and none was converted to open surgery. The duration of operation was 92.2 ± 18.8 min and the length of postoperative hospital stay was 4.4 ± 3.7 d. Postoperative bile leakage occurred in 2 cases and these patients recovered by simple drainage for 3 to 7 days without re-operation. All patients recovered smoothly without any serious complications. Conclusions: Primary suture following 3-port LCBDE is safe, effective and mini-invasive, which is worthy of further clinical application.

Image format pipeline and instrument diagram recognition method based on deep learning

In this study,we proposed a recognition method based on deep artificial neural networks to identify various elements in pipelines and instrumentation diagrams(P&ID)in image formats,such as symbols,texts,and pipelines.Presently,the P&ID image format is recognized manually,and there is a problem with a high recognition error rate;therefore,automation of the above process is an important issue in the processing plant industry.The China National Offshore Petrochemical Engineering Co.provided the image set used in this study,which contains 51 P&ID drawings in the PDF.We converted the PDF P&ID drawings to PNG P&IDs with an image size of 8410×5940.In addition,we used labeling software to annotate the images,divided the dataset into training and test sets in a 3:1 ratio,and deployed a deep neural network for recognition.The method proposed in this study is divided into three steps.The first step segments the images and recognizes symbols using YOLOv5+SE.The second step determines text regions using character region awareness for text detection,and performs character recognition within the text region using the optical character recognition technique.The third step is pipeline recognition using YOLOv5+SE.The symbol recognition accuracy was 94.52%,and the recall rate was 93.27%.The recognition accuracy in the text positioning stage was 97.26%and the recall rate was 90.27%.The recognition accuracy in the character recognition stage was 90.03%and the recall rate was 91.87%.The pipeline identification accuracy was 92.9%,and the recall rate was 90.36%.