Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery

Understanding and anticipating intraoperative events and actions is critical for intraoperative assistance and decision-making during minimally invasive surgery. Automated prediction of events, actions, and the following consequences is addressed through various computational approaches with the objective of augmenting surgeons' perception and decision-making capabilities. We propose a predictive neural network that is capable of understanding and predicting critical interactive aspects of surgical workflow from intra-abdominal video, while flexibly leveraging surgical knowledge graphs. The approach incorporates a hypergraph-transformer (HGT) structure that encodes expert knowledge into the network design and predicts the hidden embedding of the graph. We verify our approach on established surgical datasets and applications, including the detection and prediction of action triplets, and the achievement of the Critical View of Safety (CVS). Moreover, we address specific, safety-related tasks, such as predicting the clipping of cystic duct or artery without prior achievement of the CVS. Our results demonstrate the superiority of our approach compared to unstructured alternatives

Concept Graph Neural Networks for Surgical Video Understanding

We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see. This ability is crucial in application domains which entail reasoning about multiple entities and concepts, such as AI-augmented surgery. In this paper, we propose a novel way of integrating conceptual knowledge into temporal analysis tasks via temporal concept graph networks. In the proposed networks, a global knowledge graph is incorporated into the temporal analysis of surgical instances, learning the meaning of concepts and relations as they apply to the data. We demonstrate our results in surgical video data for tasks such as verification of critical view of safety, as well as estimation of Parkland grading scale. The results show that our method improves the recognition and detection of complex benchmarks as well as enables other analytic applications of interest

SAGES consensus recommendations on surgical video data use, structure, and exploration (for research in artificial intelligence, clinical quality improvement, and surgical education)

BACKGROUND: Surgery generates a vast amount of data from each procedure. Particularly video data provides significant value for surgical research, clinical outcome assessment, quality control, and education. The data lifecycle is influenced by various factors, including data structure, acquisition, storage, and sharing; data use and exploration, and finally data governance, which encompasses all ethical and legal regulations associated with the data. There is a universal need among stakeholders in surgical data science to establish standardized frameworks that address all aspects of this lifecycle to ensure data quality and purpose. METHODS: Working groups were formed, among 48 representatives from academia and industry, including clinicians, computer scientists and industry representatives. These working groups focused on: Data Use, Data Structure, Data Exploration, and Data Governance. After working group and panel discussions, a modified Delphi process was conducted. RESULTS: The resulting Delphi consensus provides conceptualized and structured recommendations for each domain related to surgical video data. We identified the key stakeholders within the data lifecycle and formulated comprehensive, easily understandable, and widely applicable guidelines for data utilization. Standardization of data structure should encompass format and quality, data sources, documentation, metadata, and account for biases within the data. To foster scientific data exploration, datasets should reflect diversity and remain adaptable to future applications. Data governance must be transparent to all stakeholders, addressing legal and ethical considerations surrounding the data. CONCLUSION: This consensus presents essential recommendations around the generation of standardized and diverse surgical video databanks, accounting for multiple stakeholders involved in data generation and use throughout its lifecycle. Following the SAGES annotation framework, we lay the foundation for standardization of data use, structure, and exploration. A detailed exploration of requirements for adequate data governance will follow

Relatório de estágio em farmácia comunitária

Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr

ICG-Lymphknoten-Mapping in der Tumorchirurgie des oberen Gastrointestinaltrakts

The importance of the assessment of the N‑status in gastric carcinoma, tumors of the gastroesophageal junction and esophageal cancer is undisputed; however, there is currently no internationally validated method for lymph node mapping in esophageal and gastric cancer. Near-infrared fluorescence imaging (NIR) is an innovative technique from the field of vibrational spectroscopy, which in combination with the fluorescent dye indocyanine green (ICG) enables intraoperative real-time visualization of anatomical structures. The ICG currently has four fields of application in oncological surgery: intraoperative real-time angiography for visualization of perfusion, lymphography for visualization of lymphatic vessels, visualization of solid tumors, and (sentinel) lymph node mapping. For imaging of the lymph drainage area and therefore the consecutive lymph nodes, peritumoral injection of ICG must be performed. Several studies have demonstrated the feasibility of peritumoral injection of ICG administered 15 min to 3 days preoperatively with subsequent intraoperative visualization of the lymph nodes. So far prospective randomized studies on the validation of the method are still lacking. In contrast, the use of ICG for lymph node mapping and visualization of sentinel lymph nodes in gastric cancer has been performed in large cohorts as well as in prospective randomized settings. Up to now, multicenter studies for ICG-guided lymph node mapping during oncological surgery of the upper gastrointestinal tract are lacking. Artificial intelligence methods can help to evaluate these techniques in an automated manner in the future as well as to support intraoperative decision making and therefore to improve the quality of oncological surgery

Do the costs of robotic surgery present an insurmountable obstacle? A narrative review

With increasing market size and rising demand, the question arises whether the high cost impedes accessibility to robotic surgery. Despite all the apparent advantages robotic surgery offers to surgeons and patients, it is imperative for healthcare providers to weigh the insufficiently documented evidence for robotics against the exorbitant price. Aside from the high acquisition cost of robotic systems, the cost of instruments and accessories, maintenance, as well as the need for training, and the impact on procedural dynamics in the operating room factor into any cost–utility analysis. However, current perspectives provide an insufficient overview of available systems and their cost. And the lack of transparency and incomplete information provided by manufacturers impose a significant challenge to informed decision-making. This article gives a short overview of the cost of robotic surgery, what additional costs to consider, where to obtain information, and attempts to elaborate on the question of whether cost impedes the worldwide establishment of robotic surgery

SUPR-GAN: SUrgical PRediction GAN for Event Anticipation in Laparoscopic and Robotic Surgery

Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation. In this work, we move beyond mere identification of past surgical phases, into the prediction of future surgical steps and specification of the transitions between them. We use a novel Generative Adversarial Network (GAN) formulation to sample future surgical phases trajectories conditioned on past video frames from laparoscopic cholecystectomy (LC) videos and compare it to state-of-the-art approaches for surgical video analysis and alternative prediction methods. We demonstrate the GAN formulation's effectiveness through inferring and predicting the progress of LC videos. We quantify the horizon-accuracy trade-off and explored average performance, as well as the performance on the more challenging, and clinically relevant transitions between phases. Furthermore, we conduct a survey, asking 16 surgeons of different specialties and educational levels to qualitatively evaluate predicted surgery phases.Comment: RA-L ICRA 202

Postesophagectomy Diaphragmatic Prolapse after Robot-Assisted Minimally Invasive Esophagectomy (RAMIE)

Background: Postesophagectomy diaphragmatic prolapse (PDP) is a major complication after esophagectomy with significant mortality and morbidity. However, in the current literature, treatment and outcomes are not evaluated for patients undergoing an Ivor Lewis Robot-assisted minimally invasive esophagectomy (IL-RAMIE). The aim of this study is to evaluate the incidence of PDP after IL-RAMIE. Moreover, the study aims to determine whether using a minimally invasive approach in the management of PDP after an IL-RAMIE procedure is safe and feasible. Materials and Methods: This study includes all patients who received an IL-RAMIE at our high-volume center (>200 esophagectomies/year) between April 2017 and December 2022 and developed PDP. The analysis focuses on time to prolapse, symptoms, treatment, surgical method, and recurrence rates of these patients. Results: A total of 185 patients underwent an IL-RAMIE at our hospital. Eleven patients (5.9%) developed PDP. Patients presented with PDP after a medium time of 241 days with symptoms like reflux, nausea, vomiting, and pain. One-third of these patients did not suffer from any symptoms. In all cases, a CT scan was performed in which the colon transversum always presented as the herniated organ. In one patient, prolapse of the small intestine, pancreas, and greater omentum also occurred. A total of 91% of these patients received a revisional surgery in a minimally invasive manner with a mean hospital stay of 12 days. In four patients, PDP recurred (36%) after 13, 114, 119 and 237 days, respectively. Conclusion: This study shows that a minimally invasive approach in repositioning PDP is a safe and effective option after IL-RAMIE