Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Background: Robot-assisted surgery (RS) has gained popularity due to its potential advantages over conventional laparoscopic surgery (LS). However, the specific suturing steps that benefit most from RS in terms of efficiency remain unclear. This study aimed to compare the suturing performance and learning curves of RS and LS during hepaticojejunostomy.

Methods: We retrospectively analyzed surgical videos of patients who underwent hepaticojejunostomy performed by the same surgeon between 2016 and 2023. Cases with incomplete data or conversion to open surgery were excluded. Suturing efficiency, anastomotic precision, and learning curves were evaluated using standardized metrics.

Results: A total of 33 patients were included in the final analysis (17 RS, 16 LS). The median suture time per stitch was significantly shorter in the RS group ( = 0.017). The greatest efficiency gains were observed at the second ( = 0.041) and final stitches ( = 0.041). Complication rates were comparable between the two groups ( = 0.986).

Conclusion: RS significantly improves efficiency at challenging suturing steps and provides a more consistent learning curve, highlighting its potential advantage for complex pediatric procedures such as hepaticojejunostomy. Future multicenter studies with larger sample sizes and longer follow-up are needed to validate these results and explore long-term outcomes.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11958166PMC
http://dx.doi.org/10.3389/fped.2025.1558362DOI Listing

Publication Analysis

Top Keywords

learning curve
8
suturing steps
8
learning curves
8
suturing
5
learning
4
curve comparison
4
comparison robot-assisted
4
robot-assisted laparoscopic
4
laparoscopic hepaticojejunostomy
4
hepaticojejunostomy focus
4

Similar Publications

Background: As a common postoperative neurological complication, postoperative delirium (POD) can lead to poor postoperative recovery in patients, prolonged hospitalization, and even increased mortality. However, POD's mechanism remains undefined and there are no reliable molecular markers of POD to date. The present work examined the associations of cerebrospinal fluid (CSF) sTREM2 with CSF POD biomarkers, and investigated whether the effects of CSF sTREM2 on POD were modulated by the core pathological indexes of POD (Aβ42, tau, and ptau).

View Article and Find Full Text PDF

Non-invasive prediction of invasive lung adenocarcinoma and high-risk histopathological characteristics in resectable early-stage adenocarcinoma by [18F]FDG PET/CT radiomics-based machine learning models: a prospective cohort Study.

Int J Surg

September 2025

Department of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Diseases, Key Laboratory of Pulmonary Diseases of National Health Commission, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

Background: Precise preoperative discrimination of invasive lung adenocarcinoma (IA) from preinvasive lesions (adenocarcinoma in situ [AIS]/minimally invasive adenocarcinoma [MIA]) and prediction of high-risk histopathological features are critical for optimizing resection strategies in early-stage lung adenocarcinoma (LUAD).

Methods: In this multicenter study, 813 LUAD patients (tumors ≤3 cm) formed the training cohort. A total of 1,709 radiomic features were extracted from the PET/CT images.

View Article and Find Full Text PDF

Objective: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance.

Methods: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument.

View Article and Find Full Text PDF

The increasing prevalence of diabetes mellitus (DM) and patients' lack of self-management awareness have led to a decline in health-related quality of life (HRQoL). Studies identifying potential risk factors for HRQoL in DM patients and presenting generalized models are relatively scarce. The study aimed to develop and evaluate a machine learning (ML)-based model to predict the HRQoL in adult diabetic patients and to examine the important factors affecting HRQoL.

View Article and Find Full Text PDF

This study aimed to develop and validate a machine learning-based predictive model for assessing the risk of fear of childbirth in pregnant women during late pregnancy. A cross-sectional observational study was conducted from November 2022 to July 2023, involving 406 pregnant women. Six machine learning algorithms, including Lasso-assisted logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGB), support vector machine (SVM), Bayesian network (BN), and k-nearest neighbors (KNN), were used to construct the models with 10-fold cross-validation.

View Article and Find Full Text PDF