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Preoperative prediction of the selection of the NOTES approach for patients with symptomatic simple renal cysts via an interpretable machine learning model: a retrospective study of 264 patients.
Langenbecks Arch Surg
January 2025
Department of Urology, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Background: There are multiple surgical approaches for treating symptomatic simple renal cysts (SSRCs). The natural orifice transluminal endoscopic surgery (NOTES) approach has gradually been applied as an emerging minimally invasive approach for the treatment of SSRCs. However, there are no clear indicators for selecting the NOTES approach for patients with SSRCs.
View Article and Find Full Text PDFA randomized cohort study on the use of 3D printed models to enhance surgical training in suturing techniques.
Sci Rep
January 2025
General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310014, China.
Three-dimensional (3D) printed surgical models provide an excellent surgical training option to closely mimic real operations to teach medical students who currently rely largely on visual learning aided with simple suturing pads. There is an unmet need to create simple to complex surgical training programs suitable for medical students. A prospective cohort study was conducted on a group of 16 6th year students.
View Article and Find Full Text PDFAnalysis and prediction of condylar resorption following orthognathic surgery.
Sci Rep
January 2025
OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium.
Condylar resorption is a feared complication of orthognathic surgery. This study investigated condylar resorption in a cohort of 200 patients This allowed for a powerful update on incidence and risk factors. 9.
View Article and Find Full Text PDFMachine learning based prediction model for bile leak following hepatectomy for liver cancer.
HPB (Oxford)
December 2024
Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States. Electronic address:
Objective: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver cancer.
Methods: An eXtreme Gradient Boosting (XGBoost) model was developed to predict post-hepatectomy bile leak using data from the ACS-NSQIP database. The model was externally validated using data from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) multi-institutional databases.
Effect of telemedicine support for intraoperative anaesthesia care on postoperative outcomes: the TECTONICS randomised clinical trial.
Br J Anaesth
January 2025
Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St Louis, St. Louis, MO, USA.
Background: Telemedicine may help improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied.
Methods: This was a single-centre randomised clinical trial of unselected adult surgical patients.
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