Abstract-Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the visual features used are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Extensive experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.

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Peri-operative SARS-CoV-2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS-CoV-2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre-operative SARS-CoV-2 infection were compared with those without previous SARS-CoV-2 infection. The primary outcome measure was 30-day postoperative mortality. Logistic regression models were used to calculate adjusted 30-day mortality rates stratified by time from diagnosis of SARS-CoV-2 infection to surgery. Among 140,231 patients (116 countries), 3127 patients (2.2%) had a pre-operative SARS-CoV-2 diagnosis. Adjusted 30-day mortality in patients without SARS-CoV-2 infection was 1.5% (95%CI 1.4-1.5). In patients with a pre-operative SARS-CoV-2 diagnosis, mortality was increased in patients having surgery within 0-2 weeks, 3-4 weeks and 5-6 weeks of the diagnosis (odds ratio (95%CI) 4.1 (3.3-4.8), 3.9 (2.6-5.1) and 3.6 (2.0-5.2), respectively). Surgery performed ≥ 7 weeks after SARS-CoV-2 diagnosis was associated with a similar mortality risk to baseline (odds ratio (95%CI) 1.5 (0.9-2.1)). After a ≥ 7 week delay in undertaking surgery following SARS-CoV-2 infection, patients with ongoing symptoms had a higher mortality than patients whose symptoms had resolved or who had been asymptomatic (6.0% (95%CI 3.2-8.7) vs. 2.4% (95%CI 1.4-3.4) vs. 1.3% (95%CI 0.6-2.0), respectively). Where possible, surgery should be delayed for at least 7 weeks following SARS-CoV-2 infection. Patients with ongoing symptoms ≥ 7 weeks from diagnosis may benefit from further delay.

ObjectiveTo show the feasibility of performing surgery across transoceanic distances by using dedicated asynchronous transfer mode (ATM) telecommunication technology. Summary Background DataTechnical limitations and the issue of time delay for transmission of digitized information across existing telecommunication lines had been a source of concern about the feasibility of performing a complete surgical procedure from remote distances. MethodsTo verify the feasibility and safety in humans, the authors attempted remote robot-assisted laparoscopic cholecystectomy on a 68-year-old woman with a history of abdominal pain and cholelithiasis. Surgeons were in New York and the patient in Strasbourg. Connections between the sites were done with a high-speed terrestrial network (ATM service). ResultsThe operation was carried out successfully in 54 minutes without difficulty or complications. Despite a round-trip distance of more than 14,000 km, the mean time lag for transmission during the procedure was 155 ms. The surgeons perceived the procedure as safe and the overall system as perfectly reliable. The postoperative course was uneventful and the patient returned to normal activities within 2 weeks after surgery. ConclusionsRemote robot-assisted surgery appears feasible and safe. Teletransmission of active surgical manipulations has the potential to ensure availability of surgical expertise in remote locations for difficult or rare operations, and to improve surgical training worldwide.Remote surgical operations require both rapid and accurate transmission of information. Factors that influence significantly the rapidity and accuracy of this information are the time required to convert video images and gestures into electronic signals, and the bandwidth and time lag of existing telecommunication lines.1,2 Using current technology, we recently showed the feasibility of performing remote surgical operations in an experimental animal model.3 Results of our experimental tests allowed us to perform, for the first time, remote robot-assisted surgery on a human. Here we present the case and postoperative course and discuss the current limitations and the potential clinical and social impact of remote telesurgery.

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