Background
The incidence of de novo gastroesophageal reflux disease (GERD) after LSG is substantial. However, an objective correlation with the structural gastric and EGJ changes has not been ...demonstrated yet. We aimed to prospectively evaluate the effects of laparoscopic sleeve gastrectomy (LSG) on the structure and function of the esophagogastric junction (EGJ) and stomach.
Methods
Investigations were performed before and after > 50% reduction in excess body weight (6–12 months after LSG). Subjects with GERD at baseline were excluded. Magnetic Resonance Imaging (MRI), high-resolution manometry (HRM), and ambulatory pH-impedance measurements were used to assess the structure and function of the EGJ and stomach before and after LSG.
Results
From 35 patients screened, 23 (66%) completed the study (age 36 ± 10 years, BMI 42 ± 5 kg/m
2
). Mean excess weight loss was 59 ± 18% after 7.1 ± 1.7-month follow-up. Esophageal acid exposure (2.4 (1.5–3.2) to 5.1 (2.8–7.3);
p
= 0.040 (normal < 4.0%)) and reflux events increased after surgery (57 ± 24 to 84 ± 38;
p
= 0.006 (normal < 80/day)). Esophageal motility was not altered by surgery; however, intrabdominal EGJ length and pressure were reduced (both
p
< 0.001); whereas the esophagogastric insertion angle (35° ± 11° to 51° ± 16°;
p
= 0.0004 (normal < 60°)) and esophageal opening diameter (16.9 ± 2.8 mm to 18.0 ± 3.7 mm;
p
= 0.029) were increased. The increase in reflux events correlated with changes in EGJ insertion angle (
p
= 0.010). Patients with > 80% reduction in gastric capacity (TGV) had the highest prevalence of symptomatic GERD.
Conclusion
LSG has multiple effects on the EGJ and stomach that facilitate reflux. In particular, EGJ disruption as indicated by increased (more obtuse) esophagogastric insertion angle and small gastric capacity were associated with the risk of GERD after LSG.
clinicaltrials.gov
:
NCT01980420
Artificial intelligence makes surgical resection easier and safer, and, at the same time, can improve oncological results. The robotic system fits perfectly with these more or less diffused ...technologies, and it seems that this benefit is mutual. In liver surgery, robotic systems help surgeons to localize tumors and improve surgical results with well-defined preoperative planning or increased intraoperative detection. Furthermore, they can balance the absence of tactile feedback and help recognize intrahepatic biliary or vascular structures during parenchymal transection. Some of these systems are well known and are already widely diffused in open and laparoscopic hepatectomies, such as indocyanine green fluorescence or ultrasound-guided resections, whereas other tools, such as Augmented Reality, are far from being standardized because of the high complexity and elevated costs. In this paper, we review all the experiences in the literature on the use of artificial intelligence systems in robotic liver resections, describing all their practical applications and their weaknesses.
Purpose
Automatic segmentation and classification of surgical activity is crucial for providing advanced support in computer-assisted interventions and autonomous functionalities in robot-assisted ...surgeries. Prior works have focused on recognizing either coarse activities, such as phases, or fine-grained activities, such as gestures. This work aims at jointly recognizing two complementary levels of granularity directly from videos, namely phases and steps.
Methods
We introduce two correlated surgical activities, phases and steps, for the laparoscopic gastric bypass procedure. We propose a multi-task multi-stage temporal convolutional network (MTMS-TCN) along with a multi-task convolutional neural network (CNN) training setup to jointly predict the phases and steps and benefit from their complementarity to better evaluate the execution of the procedure. We evaluate the proposed method on a large video dataset consisting of 40 surgical procedures (
Bypass40
).
Results
We present experimental results from several baseline models for both phase and step recognition on the
Bypass40
. The proposed MTMS-TCN method outperforms single-task methods in both phase and step recognition by 1-2% in accuracy, precision and recall. Furthermore, for step recognition, MTMS-TCN achieves a superior performance of 3-6% compared to LSTM-based models on all metrics.
Conclusion
In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on a gastric bypass dataset with multi-level annotations. The proposed method shows that the joint modeling of phases and steps is beneficial to improve the overall recognition of each type of activity.
Purpose
The coronavirus disease 2019 (COVID-19) pandemic hit Italy early and strongly, challenging the whole health care system. Proctological patients and surgeons are experiencing a previously ...unseen change in care with unknown repercussion. Here we discuss the proctological experience of 4 Italian hospitals during the COVID-19 pandemic.
Methods
Following remote brainstorming, the authors summarised their experience in managing proctological patients during the COVID-19 pandemics and put forward some practical observations to further investigate.
Results
The 4 hospitals shifted from a high-volume proctological activity to almost “zero” visits and surgery. Every patient accessing the hospital must respect a specific COVID-19 protocol. Proctological patients can be stratified based on presentation and management considerations into (1) neoplastic patients, the only allowed to be surgically treated, (2) the ones requiring urgent care, operated only in highly selected cases and (3) the stable, already known patients, managed remotely. Changes in the clinical management of the proctological disease are presented together with some considerations to be explored.
Conclusions
In the absence of scientific evidence, these practical considerations may be valuable to proctological surgeons starting to face the COVID-19 pandemics. Beside the more clinical considerations, this crisis produced unexpected consequences such as an improvement of the therapeutic alliance and a shift towards telemedicine that may be worth exploring also in the post-COVID-19 era.
Background
HSI is an optical technology allowing for a real-time, contrast-free snapshot of physiological tissue properties, including oxygenation. Hyperspectral imaging (HSI) has the potential to ...quantify the gastrointestinal perfusion intraoperatively. This experimental study evaluates the accuracy of HSI, in order to quantify bowel perfusion, and to obtain a superposition of the hyperspectral information onto real-time images.
Methods
In 6 pigs, 4 ischemic bowel loops were created (A, B, C, D) and imaged at set time points (from 5 to 360 min). A commercially available HSI system provided pseudo-color maps of the perfusion status (StO2, Near-InfraRed perfusion) and the tissue water index. An ad hoc software was developed to superimpose HSI information onto the live video, creating the HYPerspectral-based Enhanced Reality (HYPER). Seven regions of interest (ROIs) were identified in each bowel loop according to StO2 ranges, i.e., vascular (VASC proximal and distal), marginal vascular (MV proximal and distal), marginal ischemic (MI proximal and distal), and ischemic (ISCH). Local capillary lactates (LCL), reactive oxygen species (ROS), and histopathology were measured at the ROIs. A machine-learning-based prediction algorithm of LCL, based on the HSI-StO2%, was trained in the 6 pigs and tested on 5 additional animals.
Results
HSI parameters (StO2 and NIR) were congruent with LCL levels, ROS production, and histopathology damage scores at the ROIs discriminated by HYPER. The global mean error of LCL prediction was 1.18 ± 1.35 mmol/L. For StO2 values > 30%, the mean error was 0.3 ± 0.33.
Conclusions
HYPER imaging could precisely quantify the overtime perfusion changes in this bowel ischemia model.
Background
As flexible endoscopy offers many advantages to patients, access to training should be aggressively encouraged. In 2014, the IRCAD-IHU-Strasbourg launched a year-long university diploma ...using advanced education methods to offer surgeons and gastroenterologists high-quality, personalized training in flexible endoscopy. This paper describes and critically reviews the first 5 years of the University Diploma in Surgical Endoscopy (UDSE).
Methods
The UDSE aims to progressively transmit theoretical knowledge, clinical judgment, and practical skills on basic and advanced flexible endoscopy. The 300-h year-long curriculum is composed of 100 h of online lectures with tests, 150 h of clinical rotations and 50 h of hands-on sessions. The hands-on training is delivered through validated mechanical simulators, virtual reality simulators, and specifically designed
ex vivo
and in vivo animal models. Participants’ demographics, training, and clinical experience were recorded. Trainees’ evaluations of each online lecture, hands-on training, and clinical rotations were assessed using a Likert scale from 1 (not satisfactory) to 5 (outstanding). Trainees’ skill progression was evaluated using the Global Assessment of Gastrointestinal Endoscopic Skills (GAGES) proficiency test. Finally, clinical uptake was surveyed.
Results
162 (79.01% males) trainees from 38 countries enrolled and successfully completed the first 5 courses. The vast majority of the trainees were surgeons and 19.14% were gastroenterologist. Sixty-nine (42.59%) participants were residents and 97 (56.79%) had no prior experience in flexible endoscopy. The online lectures, on-site sessions, and clinical rotations were highly appreciated receiving an overall average score of 4.33/5, 4.56/5, 4.43/5, respectively. Trainees’ endoscopic skills improved significantly (16.68 vs. 20.53 GAGES scores;
p
= 0.016). At an average of 18.83 months following the course, 31 alumni (77.50% of repliers) started to use a flexible endoscope in their practice.
Conclusions
Over its 5-year evolution, the UDSE has proven to be a valid means to ease access to the fundamental knowledge, practical skills, and clinical judgment necessary to achieve proficiency in surgical endoscopy.
Endoscopic retrograde cholangiopancreatography (ERCP) is an advanced endoscopic procedure that might lead to severe adverse events. Post-ERCP pancreatitis (PEP) is the most common post-procedural ...complication, which is related to significant mortality and increasing healthcare costs. Up to now, the prevalent approach to prevent PEP consisted of employing pharmacological and technical expedients that have been shown to improve post-ERCP outcomes, such as the administration of rectal nonsteroidal anti-inflammatory drugs, aggressive intravenous hydration, and the placement of a pancreatic stent. However, it has been reported that PEP originates from a more complex interaction of procedural and patient-related factors. Appropriate ERCP training has a pivotal role in PEP prevention strategy, and it is not a chance that a low PEP rate is universally considered one of the most relevant indicators of proficiency in ERCP. Scant data on the acquisition of skills during the ERCP training are currently available, although some efforts have been recently done to shorten the learning curve by way of simulation-based training and demonstrate competency by meeting technical requirements as well as adopting skill evaluation scales. Besides, the identification of adequate indications for ERCP and accurate pre-procedural risk stratification of patients might help to reduce PEP occurrence regardless of the endoscopist’s technical abilities, and generally preserve safety in ERCP. This review aims at delineating current preventive strategies and highlighting novel perspectives for a safer ERCP focusing on the prevention of PEP.
Background
In laparoscopic cholecystectomy (LC), achievement of the Critical View of Safety (CVS) is commonly advocated to prevent bile duct injuries (BDI). However, BDI rates remain stable, probably ...due to inconsistent application or a poor understanding of CVS as well as unreliable reporting. Objective video reporting could serve for quality auditing and help generate consistent datasets for deep learning models aimed at intraoperative assistance. In this study, we develop and test a method to report CVS using videos.
Method
LC videos performed at our institution were retrieved and the video segments starting 60 s prior to the division of cystic structures were edited. Two independent reviewers assessed CVS using an adaptation of the doublet view 6-point scale and a novel binary method in which each criterion is considered either achieved or not. Feasibility to assess CVS in the edited video clips and inter-rater agreements were evaluated.
Results
CVS was attempted in 78 out of the 100 LC videos retrieved. CVS was assessable in 100% of the 60-s video clips. After mediation, CVS was achieved in 32/78(41.03%). Kappa scores of inter-rater agreements using the doublet view versus the binary assessment were as follows: 0.54 versus 0.75 for CVS achievement, 0.45 versus 0.62 for the dissection of the hepatocystic triangle, 0.36 versus 0.77 for the exposure of the lower part of the cystic plate, and 0.48 versus 0.79 for the 2 structures connected to the gallbladder.
Conclusions
The present study is the first to formalize a reproducible method for objective video reporting of CVS in LC. Minute-long video clips provide information on CVS and binary assessment yields a higher inter-rater agreement than previously used methods. These results offer an easy-to-implement strategy for objective video reporting of CVS, which could be used for quality auditing, scientific communication, and development of deep learning models for intraoperative guidance.
Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if the endoscopic camera is moved ...out of the body of patients and out-of-body scenes are recorded. Therefore, identification of out-of-body scenes in endoscopic videos is of major importance to preserve the privacy of patients and operating room staff. This study developed and validated a deep learning model for the identification of out-of-body images in endoscopic videos. The model was trained and evaluated on an internal dataset of 12 different types of laparoscopic and robotic surgeries and was externally validated on two independent multicentric test datasets of laparoscopic gastric bypass and cholecystectomy surgeries. Model performance was evaluated compared to human ground truth annotations measuring the receiver operating characteristic area under the curve (ROC AUC). The internal dataset consisting of 356,267 images from 48 videos and the two multicentric test datasets consisting of 54,385 and 58,349 images from 10 and 20 videos, respectively, were annotated. The model identified out-of-body images with 99.97% ROC AUC on the internal test dataset. Mean ± standard deviation ROC AUC on the multicentric gastric bypass dataset was 99.94 ± 0.07% and 99.71 ± 0.40% on the multicentric cholecystectomy dataset, respectively. The model can reliably identify out-of-body images in endoscopic videos and is publicly shared. This facilitates privacy preservation in surgical video analysis.
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