Pietro Mascagni

Policlinico Universitario Agostino Gemelli · Department of General Surgery

Background/objectives: Insulinomas are rare, functioning pancreatic neuroendocrine neoplasms (pNEN), whose gold standard therapy is surgical resection. Endoscopic ultrasound-guided radiofrequency ablation (EUS-RFA) is a recent technique that has emerged as a minimally invasive therapeutic option for patients with pancreatic lesions not eligible fo...

Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of current vision-based activity recognition methods relies heavily on a high volume of manually annotated data. This data is difficult and time-consuming to generate and req...

Purpose: Automatic recognition of surgical activities from intraoperative surgical videos is crucial for developing intelligent support systems for computer-assisted interventions. Current state-of-the-art recognition methods are based on deep learning where data augmentation has shown the potential to improve the generalization of these methods....

Perirectal hematoma (PH) is one of the most feared complications of stapling procedures. Literature reviews have reported only a few works on PH, most of them describing isolated treatment approaches and severe outcomes. The aim of this study was to analyze a homogenous case series of PH and to define a treatment algorithm for huge postoperative PH...

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 emp...

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet t...

Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of current vision-based activity recognition methods relies heavily on a high volume of manually annotated data. This data is difficult and time-consuming to generate and req...

Background: Shared decision-making (SDM) between clinicians and patients is one of the pillars of the modern patient-centric philosophy of care. This study aims to explore SDM in the discipline of trauma and emergency surgery, investigating its interpretation as well as the barriers and facilitators for its implementation among surgeons. Methods:...

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence...

Background Surgical video recording provides the opportunity to acquire intraoperative data that can subsequently be used for a variety of quality improvement, research, and educational applications. Various recording devices are available for standard operating room camera systems. Some allow for collateral data acquisition including activities of...

Objective: To develop and validate a deep learning model for the identification of out-of-body images in endoscopic videos. Background: Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if out-of-body scenes are recorded. Therefore, ide...

Background: Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergenc...

Artificial intelligence is set to be deployed in operating rooms to improve surgical care. This early-stage clinical evaluation shows the feasibility of concurrently attaining real-time, high-quality predictions from several deep neural networks for endoscopic video analysis deployed for assistance during three laparoscopic cholecystectomies.

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. In this work, we propose to capture each of these aspects by modeling the surgical scene wi...

The use of digital technology is increasing rapidly across surgical specialities, yet there is no consensus for the term ‘digital surgery’. This is critical as digital health technologies present technical, governance, and legal challenges which are unique to the surgeon and surgical patient. We aim to define the term digital surgery and the ethica...

Background Artificial Intelligence (AI) is set to impact several fields within gastroenterology. In gastrointestinal (GI) endoscopy, AI-based tools have translated into clinical practice faster than expected. We aimed to evaluate the status of research for AI in Gastroenterology while predicting its future applications. Methods All studies registe...

Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering the sensitive nature of medical...

Objective: To review artificial intelligence (AI) based applications for the assessment of technical skills in minimally invasive surgery. Background: As technical skill assessment in surgery relies on expert opinion, it is time-consuming, costly, and often lacks objectivity. Analysis of routinely generated data by AI methods has the potential for...

Purpose Insulinomas are rare, functioning pancreatic neuroendocrine neoplasms (pNEN), whose gold standard therapy is surgical resection. Endoscopic ultrasound-guided radiofrequency ablation (EUS-RFA) is a recent technique that has emerged as minimally invasive therapeutic option for patients with pancreatic lesions not eligible for surgery. In this...

Hundreds of millions of operations are performed worldwide each year, and the rising uptake in minimally invasive surgery has enabled fiber optic cameras and robots to become both important tools to conduct surgery and sensors from which to capture information about surgery. Computer vision (CV), the application of algorithms to analyze and interpr...

Artificial intelligence (AI) and computer vision (CV) are beginning to impact medicine. While evidence on the clinical value of AI-based solutions for the screening and staging of colorectal cancer (CRC) is mounting, CV and AI applications to enhance the surgical treatment of CRC are still in their early stage. This manuscript introduces key AI con...

Background During laparoscopy, the abdominal cavity is insufflated with carbon dioxide (CO 2 ) that could become contaminated with viruses and surgical smoke. Medical staff is potentially exposed when this gas leaks into the operating room through the instruments and past trocar valves. No detailed studies currently exist that have quantified these...

This article was updated to correct the order of the author listing.

Background: Artificial intelligence (AI) applications aiming to support surgical decision-making processes are generating novel threats to ethical surgical care. To understand and address these threats, we summarize the main ethical issues that may arise from applying AI to surgery, starting from the Ethics Guidelines for Trustworthy Artificial In...

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet t...

Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering the sensitive nature of medical...

Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care. However the primitive and most common approach to retrieval, involving text in the form of keywords, is severely limited when dealing with complex media formats. Content-based retrieval offers a way to overcome this...

Background: A considerable number of surgical residents fail the mandated endoscopy exam despite having completed the required clinical cases. Low-cost endoscopy box trainers (BTs) could democratize training; however, their effectiveness was never compared with higher-cost virtual reality simulators (VRSs). Study design: In this randomized nonin...

Out of all existing frameworks for surgical workflow analysis in endoscopic videos, action triplet recognition stands out as the only one aiming to provide truly fine-grained and comprehensive information on surgical activities. This information, presented as 〈instrument, verb, target〉 combinations, is highly challenging to be accurately identified...

BackgroundA computer vision (CV) platform named EndoDigest was recently developed to facilitate the use of surgical videos. Specifically, EndoDigest automatically provides short video clips to effectively document the critical view of safety (CVS) in laparoscopic cholecystectomy (LC). The aim of the present study is to validate EndoDigest on a mult...

Aim We aimed to evaluate the knowledge, attitude, and practices in the application of AI in the emergency setting among international acute care and emergency surgeons. Methods An online questionnaire composed of 30 multiple choice and open-ended questions was sent to the members of the World Society of Emergency Surgery between 29th May and 28th...

Background Laparoscopic endoscopic cooperative colorectal surgery (LECS-CR) is a promising technique to achieve full-thickness resection of colorectal tumors. This approach has shown good rates of complete resection and low local recurrence, especially for large laterally spreading tumors, which are difficult to remove via endoscopy alone. However,...

A major obstacle to building models for effective semantic segmentation, and particularly video semantic segmentation, is a lack of large and well annotated datasets. This bottleneck is particularly prohibitive in highly specialized and regulated fields such as medicine and surgery, where video semantic segmentation could have important application...

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 wel...

Aims The increased use of endoscopy as a minimally invasive therapeutic technique has created a great demand for endoscopic training. The Basic Endoscopic Skills Training (BEST) box provides a low-cost solution by adapting the Fundamentals of Laparoscopic Surgery (FLS) box for flexible endoscopic simulation. The BEST box consists of six endoscopic...

Background Laparoscopic cholecystectomy operative difficulty is highly variable and influences outcomes. This systematic review analyzes the performance and clinical value of statistical models to preoperatively predict laparoscopic cholecystectomy operative difficulty. Methods PRISMA guidelines were followed. PubMed, Embase, and the Cochrane Libr...

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing num...

Aim: We aimed to evaluate the knowledge, attitude and practices in the application of artificial intelligence in the emergency setting among international acute care and emergency surgeons. Methods: An online questionnaire composed of 30 multiple choice and open-ended questions was sent to the members of the World Society of Emergency Surgery betwe...

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous...

Out of all existing frameworks for surgical workflow analysis in endoscopic videos, action triplet recognition stands out as the only one aiming to provide truly fine-grained and comprehensive information on surgical activities. This information, presented as <instrument, verb, target> combinations, is highly challenging to be accurately identified...

Background: The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration. M...

Aim: Hemorrhoidectomy is still the most effective surgical treatment for hemorrhoidal disease, but it is, however, associated with complications such as pain and stenosis. We proposed to break the “vicious circle” of “pain–sphincteric spasm–stenosis–pain” with the postoperative use of self-mechanical anal dilation. Methods: We retrospectively analy...

Surgical data science (SDS) aims to improve the quality of interventional healthcare and its value through the capture, organization, analysis, and modeling of procedural data. As data capture has increased and artificial intelligence (AI) has advanced, SDS can help to unlock augmented and automated coaching, feedback, assessment, and decision supp...

Background Endoscopic Submucosal Dissection (ESD) is the treatment of choice of superficial neoplastic gastrointestinal lesions. Delayed bleedings and perforations are still current clinical concerns. Glubran 2 is a synthetic cyanoacrylate-derived glue nowadays already widely used as an effective tissue adhesive. ENDONEB is a novel device thought f...

Background The critical view of safety (CVS) is poorly adopted in surgical practices although it is ubiquitously recommended to prevent major bile duct injuries during laparoscopic cholecystectomy (LC). This study aims to investigate whether performing a short intraoperative time out can improve CVS implementation. Study design In this before vers...

Minimally invasive image-guided surgery heavily relies on vision. Deep learning models for surgical video analysis could therefore support visual tasks such as assessing the critical view of safety (CVS) in laparoscopic cholecystectomy (LC), potentially contributing to surgical safety and efficiency. However, the performance, reliability and reprod...

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 describe...

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...

Background: The critical view of safety (CVS) is poorly adopted in surgical practices although it is ubiquitously recommended to prevent major bile duct injuries during laparoscopic cholecystectomy (LC). This study aims to determine whether performing a short intraoperative time out can improve CVS implementation. Methods: Surgeons performing LCs a...

Introduction: Stapled hemorrhoidopexy was originally defined as a rectal mucosectomy. The aims of our retrospective, single-center study were to demonstrate if the excised specimen comprises only the mucosa or more wall rectal layers and if the latter excision should be considered a technical mistake with an increase in complications. Materials and...

Résumé Une chirurgie efficace et sûre est le résultat d’un processus sociétal et technique complexe source d’erreurs humaines. L’acquisition d’une grande quantité de données sur les soins chirurgicaux et la modélisation des procédures chirurgicales utilisant les méthodes de calcul de l’intelligence artificielle pourraient éclairer sur les contraint...

Effective and safe surgery results from a complex sociotechnical process prone to human error. Acquiring large amount of data on surgical care and modelling the process of surgery with artificial intelligence's computational methods could shed lights on system strengths and limitations and enable computer-based smart assistance. With this vision in...

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 aim...

To reduce the risk of pancreatic fistula after pancreatectomy, a satisfactory blood flow at the pancreatic stump is considered crucial. Our group has developed and validated a real-time computational imaging analysis of tissue perfusion, using fluorescence imaging, the fluorescence-based enhanced reality (FLER). Hyperspectral imaging (HSI) is anoth...

Objective: To develop a computer vision platform to automatically locate critical events in surgical videos and provide short video clips documenting the critical view of safety (CVS) in laparoscopic cholecystectomy (LC). Background: Intraoperative events are typically documented through operator-dictated reports that do not always translate the...

The fields of computer vision (CV) and artificial intelligence (AI) have undergone rapid advancements in the past decade, many of which have been applied to the analysis of intraoperative video. These advances are driven by wide-spread application of deep learning, which leverages multiple layers of neural networks to teach computers complex tasks....

Effectiveness of computer vision techniques has been demonstrated through a number of applications, both within and outside healthcare. The operating room environment specifically is a setting with rich data sources compatible with computational approaches and high potential for direct patient benefit. The aim of this review is to summarize major t...

Objective: To develop a deep learning model to automatically segment hepatocystic anatomy and assess the criteria defining the critical view of safety (CVS) in laparoscopic cholecystectomy (LC). Background: Poor implementation and subjective interpretation of CVS contributes to the stable rates of bile duct injuries in LC. As CVS is assessed vis...

Background: Colonic capsule endoscopy (CCE) derived from the video capsule endoscopy, initially proposed to explore the small bowel, has demonstrated high sensitivity and specificity for colonic polyp detection. The primary outcome of the study was to assess the safety, feasibility, and reliability of CCE after colorectal surgery. Secondary outcom...

Background Fluorescence-based enhanced reality (FLER) enables the quantification of fluorescence signal dynamics, which can be superimposed onto real-time laparoscopic images by using a virtual perfusion cartogram. The current practice of perfusion assessment relies on visualizing the bowel serosa. The aim of this experimental study was to quantify...

Background Three-dimensional (3-D) high-definition (HD) stereovision and two-dimensional (2-D) ultra-high-resolution (4K) monitors have recently become available for laparoscopic surgery. The aim of this study was to compare laparoscopic performance between inexperienced participants using 3-D/HD and 2-D/4K monitors and those using conventional 2-D...

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical data science is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of...