is a frequent complication after open abdominal surgery. Prophylactic mesh implantation in the onlay or sublay position requires dissection of the abdominal wall, potentially leading to wound-associated complications. OBJECTIVE To compare the incidence of incisional hernia among patients after prophylactic intraperitoneal mesh implantation with that among patients after standard abdominal closure. DESIGN, SETTING, AND PARTICIPANTS An open-label randomized clinical trial was performed in 169 patients undergoing elective open abdominal surgery from January 1, 2011, to February 29, 2014. Follow-up examinations were performed 1 year and 3 years after surgery. The study was conducted at

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Laparoscopic IPOM is associated with reduced morbidity compared to open IPOM for incisional hernia repair.

Surgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.

Purpose Cholecystectomy is one of the most common laparoscopic procedures. A critical phase of laparoscopic cholecystectomy consists in clipping the cystic duct and artery before cutting them. Surgeons can improve the clipping safety by ensuring full visibility of the clipper, while enclosing the artery or the duct with the clip applier jaws. This can prevent unintentional interaction with neighboring tissues or clip misplacement. In this article, we present a novel real-time feedback to ensure safe visibility of the instrument during this critical phase. This feedback incites surgeons to keep the tip of their clip applier visible while operating. Methods We present a new dataset of 300 laparoscopic cholecystectomy videos with frame-wise annotation of clipper tip visibility. We further present ClipAssistNet, a neural network-based image classifier which detects the clipper tip visibility in single frames. ClipAssistNet ensembles predictions from 5 neural networks trained on different subsets of the dataset. Results Our model learns to classify the clipper tip visibility by detecting its presence in the image. Measured on a separate test set, ClipAssistNet classifies the clipper tip visibility with an AUROC of 0.9107, and 66.15% specificity at 95% sensitivity. Additionally, it can perform real-time inference (16 FPS) on an embedded computing board; this enables its deployment in operating room settings. Conclusion This work presents a new application of computer-assisted surgery for laparoscopic cholecystectomy, namely real-time feedback on adequate visibility of the clip applier. We believe this feedback can increase surgeons’ attentiveness when departing from safe visibility during the critical clipping of the cystic duct and artery.

Octogenarians undergoing emergency abdominal surgery had a mortality rate of 16.4%. Mortality was independently predicted by age, ASA score≥4, mesenteric ischemia and ICU admission. Therefore, the indication for emergency abdominal surgery should be assessed cautiously, including patients' and relatives' wishes, surgeons, intensivists, anesthesiologists, and nursing staff.

Background In trauma patients, the impact of inter-hospital transfer has been widely studied. However, for patients undergoing emergency abdominal surgery (EAS), the effect of inter-hospital transfer on outcomes is largely unknown. Methods This is a single-center, retrospective observational study. Outcomes of transferred patients undergoing EAS were compared to patients primarily admitted to a tertiary care hospital from 01/2016 to 12/2018 using univariable and multivariable analyses. The primary outcome was in-hospital mortality. Results Some 973 patients with a median (IQR) age of 58.1 (39.4–72.2) years and a median body mass index of 25.8 (22.5–29.3) kg/m2 were included. The transfer group comprised 258 (26.3%) individuals and the non-transfer group 715 (72.7%). The population was stratified in three subgroups: (1) patients with low surgical stress (n = 483, 49.6%), (2) with hollow viscus perforation (n = 188, 19.3%) and (3) with potential bowel ischemia (n = 302, 31.1%). Neither in the low surgical stress nor in the hollow viscus perforation group was the transfer status associated with mortality. However, in the potential bowel ischemia group inter-hospital transfer was a predictor for mortality (OR 3.54, 95%CI 1.03–12.12, p = 0.045). Moreover, in the hollow viscus perforation group inter-hospital transfer was a predictor for reduced hospital length of stay (RC -10.02, 95%CI −18.14/−1.90, p = 0.016) and reduced severe complications (OR 0.38, 95%CI 0.18–0.77, p = 0.008). Conclusion Other than in patients with low surgical stress or hollow viscus perforation, in patients with potential bowel ischemia inter-hospital transfer was an independent predictor for higher mortality. Taking into account the time sensitiveness of bowel ischemia, efforts should be made to avoid inter-hospital transfer in this vulnerable subgroup of patients.

Automated recognition of surgical phases is a prerequisite for computer-assisted analysis of surgeries. The research on phase recognition has been mostly driven by publicly available datasets of laparoscopic cholecystectomy (Lap Chole) videos. Yet, videos observed in real-world settings might contain challenges, such as additional phases and longer videos, which may be missing in curated public datasets. In this work, we study (i) the possible data distribution discrepancy between videos observed in a given medical center and videos from existing public datasets, and (ii) the potential impact of this distribution difference on model development. To this end, we gathered a large, private dataset of 384 Lap Chole videos. Our dataset contained all videos, including emergency surgeries and teaching cases, recorded in a continuous time frame of five years. We observed strong differences between our dataset and the most commonly used public dataset for surgical phase recognition, Cholec80. For instance, our videos were much longer, included additional phases, and had more complex transitions between phases. We further trained and compared several state-of-the-art phase recognition models on our dataset. The models’ performances greatly varied across surgical phases and videos. In particular, our results highlighted the challenge of recognizing extremely under-represented phases (usually missing in public datasets); the major phases were recognized with at least 76 percent recall. Overall, our results highlighted the need to better understand the distribution of the video data phase recognition models are trained on.