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Background: Laparoscopic sleeve gastrectomy (LSG) is the most common surgical treatment for morbid obesity. While certain specialized ambulatory surgery centers offer LSG on an outpatient basis, patients undergoing LSG at most academic centers are admitted to hospital for initial postoperative convalescence and monitoring. Our institution has begun to offer LSG with same-day discharge (SDD) in select patients. We aimed to compare the perioperative outcomes and costs for patients undergoing LSG with inpatient admission versus SDD., Methods: All patients enrolled in the SDD program from December 2020 through July 2022 were identified from a prospectively maintained database. Patients enrolled in this pathway were analyzed on an intention-to-treat basis even if ultimately admitted postoperatively. Propensity scoring was used to match these patients 1:1 to those with planned inpatient recovery based on age, BMI, and ASA classification., Results: Seventy-five patients were enrolled in the LSG with SDD program during the study period. Among these, 62 patients (82.7%) had successful immediate postoperative discharge. Reasons for cancelation of planned SDD included anxiety (n = 5), pain (n = 3), nausea (n = 2), and one patient each with hypotension, urinary retention, and bleeding. After matching, there were no differences in age, BMI, or ASA classification in a comparison group of patients with planned inpatient recovery. There were no differences in perioperative complications. There were no readmissions or requirements for outpatient intravenous fluids among patients with SDD, compared to n = 3 (4.0%) and n = 2 (2.7%) in the inpatient cohort, respectively. The total perioperative cost for patients undergoing LSG with planned SDD was 6.8% less than those with inpatient recovery., Conclusion: With appropriate protocols, LSG with same-day discharge can safely be performed at large academic surgery centers without increased morbidity or need for additional services in the perioperative period. SDD may be associated with decreased costs and allows for more efficient hospital bed allocation., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Artificial intelligence (AI) holds tremendous potential to reduce surgical risks and improve surgical assessment. Machine learning, a subfield of AI, can be used to analyze surgical video and imaging data. Manual annotations provide veracity about the desired target features. Yet, methodological annotation explorations are limited to date. Here, we provide an exploratory analysis of the requirements and methods of instrument annotation in a multi-institutional team from two specialized AI centers and compile our lessons learned., Methods: We developed a bottom-up approach for team annotation of robotic instruments in robot-assisted partial nephrectomy (RAPN), which was subsequently validated in robot-assisted minimally invasive esophagectomy (RAMIE). Furthermore, instrument annotation methods were evaluated for their use in Machine Learning algorithms. Overall, we evaluated the efficiency and transferability of the proposed team approach and quantified performance metrics (e.g., time per frame required for each annotation modality) between RAPN and RAMIE., Results: We found a 0.05 Hz image sampling frequency to be adequate for instrument annotation. The bottom-up approach in annotation training and management resulted in accurate annotations and demonstrated efficiency in annotating large datasets. The proposed annotation methodology was transferrable between both RAPN and RAMIE. The average annotation time for RAPN pixel annotation ranged from 4.49 to 12.6 min per image; for vector annotation, we denote 2.92 min per image. Similar annotation times were found for RAMIE. Lastly, we elaborate on common pitfalls encountered throughout the annotation process., Conclusions: We propose a successful bottom-up approach for annotator team composition, applicable to any surgical annotation project. Our results set the foundation to start AI projects for instrument detection, segmentation, and pose estimation. Due to the immense annotation burden resulting from spatial instrumental annotation, further analysis into sampling frequency and annotation detail needs to be conducted., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Artificial intelligence (AI) methods and AI-enabled metrics hold tremendous potential to advance surgical education. Our objective was to generate consensus guidance on specific needs for AI methods and AI-enabled metrics for surgical education., Study Design: The study included a systematic literature search, a virtual conference, and a 3-round Delphi survey of 40 representative multidisciplinary stakeholders with domain expertise selected through purposeful sampling. The accelerated Delphi process was completed within 10 days. The survey covered overall utility, anticipated future (10-year time horizon), and applications for surgical training, assessment, and feedback. Consensus was agreement among 80% or more respondents. We coded survey questions into 11 themes and descriptively analyzed the responses., Results: The respondents included surgeons (40%), engineers (15%), affiliates of industry (27.5%), professional societies (7.5%), regulatory agencies (7.5%), and a lawyer (2.5%). The survey included 155 questions; consensus was achieved on 136 (87.7%). The panel listed 6 deliverables each for AI-enhanced learning curve analytics and surgical skill assessment. For feedback, the panel identified 10 priority deliverables spanning 2-year (n = 2), 5-year (n = 4), and 10-year (n = 4) timeframes. Within 2 years, the panel expects development of methods to recognize anatomy in images of the surgical field and to provide surgeons with performance feedback immediately after an operation. The panel also identified 5 essential that should be included in operative performance reports for surgeons., Conclusions: The Delphi panel consensus provides a specific, bold, and forward-looking roadmap for AI methods and AI-enabled metrics for surgical education., (Copyright © 2022 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.)

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 number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process., Competing Interests: Declaration of Competing Interest Anand Malpani is a future employee at Mimic Technologies Inc. (Seattle, WA, US). Johannes Fallert and Lars Mündermann are employed at KARL STORZ SE & Co. KG (Tuttlingen, Germany). Hirenkumar Nakawala is employed at CMR Surgical Ltd (Cambridge, UK). Nicolas Padoy is a scientific advisor of Caresyntax (Berlin, Germany). Daniel A. Hashimoto is a consultant for Johnson & Johnson (New Brunswick, NJ, USA), Verily Life Sciences (San Francisco, CA, USA), and Activ Surgical (Boston, MA, USA). He has received research support from Olympus Corporation and the Intuitive Foundation. Carla Pugh is the founder of 10 Newtons Inc. (Madison, WI, US). Danail Stoyanov is employed at Digital Surgery Ltd (London, UK) and Odin Vision Ltd (London, UK). Teodor Grantcharov is the founder of Surgical Safety Technologies Inc. (Toronto, Ontario, Canada). Tobias Roß is employed at Quality Match GmbH (Heidelberg, Germany). All other authors do not declare any conflicts of interest., (Copyright © 2021. Published by Elsevier B.V.)

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. Prior to these advances, applications of AI in the operating room were limited by our relative inability to train computers to accurately understand images with traditional machine learning (ML) techniques. The development and refining of deep neural networks that can now accurately identify objects in images and remember past surgical events has sparked a surge in the applications of CV to analyze intraoperative video and has allowed for the accurate identification of surgical phases (steps) and instruments across a variety of procedures. In some cases, CV can even identify operative phases with accuracy similar to surgeons. Future research will likely expand on this foundation of surgical knowledge using larger video datasets and improved algorithms with greater accuracy and interpretability to create clinically useful AI models that gain widespread adoption and augment the surgeon's ability to provide safer care for patients everywhere., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence.The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.

Background: Sleeve gastrectomy is a relatively new procedure that developed as a result of rapid innovation in the field of bariatric surgery. As with any newly developed operation, there is a learning curve that potentially can be associated with higher morbidity. Real-time surgical mentoring reduces the learning curve effect but can be time intensive for the mentor. The aim of this initiative was to evaluate the feasibility, effectiveness, and satisfaction of surgical telementoring for laparoscopic sleeve gastrectomy. This is the first national specialty society effort to determine if the "remote presence" of an expert surgeon (mentor) can help practicing surgeons improve skills., Methods: The experience of 15 surgical trainees (mentees) who performed laparoscopic sleeve gastrectomy under real-time telementoring by 7 mentors was reviewed. Telementoring was implemented using the Visitor1 ® remote presence system with two-way live audio and video communication. The receiving platform utilized a conventional laptop, iPad, or iPhone. The mentee followed a structured telementoring program including didactic learning, live case teleobservation, and telementoring of 2-3 cases. A survey on the quality of the telecommunication and effectiveness of the mentoring was performed by the mentor and mentee on a scale of "exceeded," "met," "almost met," or "failed to meet" expectations. The overall telementoring experience was rated on a scale of 1 for "poor" to 5 for "excellent.", Results: Based on the mentees' survey, the overall telementoring experience was rated as 4.8. Despite the mentees having experience with laparoscopic sleeve gastrectomy, most commented that the telementoring experience was an excellent educational tool and they learned some new techniques they plan to apply it in their practice. Based on the mentors' survey, the overall telementoring experience was rated as 4.7. All mentors stated that they were satisfied with the telementoring sessions and there were no unexpected intraoperative occurrences. There were some logistical limitations including difficulties in scheduling of cases or the delay of cases., Conclusions: Surgical instruction by telementoring was shown to be feasible, practical, and successful, and was highly rated in this study by both the mentors and mentees. The currently utilized telementoring platform is thus an effective educational tool that can facilitate acquisition of surgical skills and assist with the conventional on-site surgical mentoring model.

Background: The goal of telementoring is to recreate face-to-face encounters with a digital presence. Open-surgery telementoring is limited by lack of surgeon's point-of-view cameras. Google Glass is a wearable computer that looks like a pair of glasses but is equipped with wireless connectivity, a camera, and viewing screen for video conferencing. This study aimed to assess the safety of using Google Glass by assessing the video quality of a telementoring session., Methods: Thirty-four (n = 34) surgeons at a single institution were surveyed and blindly compared via video captured with Google Glass versus an Apple iPhone 5 during the open cholecystectomy portion of a Whipple. Surgeons were asked to evaluate the quality of the video and its adequacy for safe use in telementoring., Results: Thirty-four of 107 invited surgical attendings (32%) responded to the anonymous survey. A total of 50% rated the Google Glass video as fair with the other 50% rating it as bad to poor. A total of 52.9% of respondents rated the Apple iPhone video as good. A significantly greater proportion of respondents felt Google Glass video quality was inadequate for telementoring versus the Apple iPhone's (82.4 vs 26.5%, p < 0.0001). Intraclass correlation coefficient was 0.924 (95% CI 0.660-0.999, p < 0.001)., Conclusion: While Google Glass provides a great breadth of functionality as a wearable device with two-way communication capabilities, current hardware limitations prevent its use as a telementoring device in surgery as the video quality is inadequate for safe telementoring. As the device is still in initial phases of development, future iterations or competitor devices may provide a better telementoring application for wearable devices.

Background: Bariatric surgery provides for a reliable and sustainable solution to the obesity epidemic. The gold standard bariatric surgical procedure is the Roux-en-Y gastric bypass (RYGB). Assessment of this population preoperatively and work-up of postoperative complications often includes radiographic evaluation. Repeated exposure to radiation is not without complication., Objective: Assess the association between the RYGB and exposure to ionizing radiation., Setting: Academic medical center., Methods: Patients were identified by their ICD-9 code as having had a RYGB at the Massachusetts General Hospital (MGH) from 2002 to 2012. The number of abdominal and pelvis (A/P) computed tomography (CT) scans performed was determined and converted into an effective dose (ED) and expressed as milliSeiverts (mSv) to illustrate the biologic effects of radiation., Results: From 2002 to 2012, 1789 primary laparoscopic RYGBs were completed. Fifty-five revisional operations were completed on 51 patients. Of these, 38 had both their index and second operation at the MGH. A total of 1065 A/P CTs were completed in the laparoscopic RYGB population (mean = .6), and 106 A/P CTs were done in the revisional surgery cohort (mean = 2.8). The mean ED of radiation was 56.1 mSv and 19.5 mSv for the index and revisional populations, respectively., Conclusions: This study demonstrated the significant cumulative radiation exposure attributable to A/P CTs. This exposes the patient to a potential increased risk of malignancy as well as imposing a financial burden on the healthcare system. The findings of this study raise the awareness of an increased risk of radiation exposure for this population and the necessity of creation of a dedicated algorithm for the mindful utilization of CT imaging., (Copyright © 2015 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.)