Safety and cost of performing laparoscopic sleeve gastrectomy with same day discharge at a large academic hospital.

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.

Concept Graph Neural Networks for Surgical Video Understanding.

Analysis of relations between objects and comprehension of abstract concepts in the surgical video is important in AI-augmented surgery. However, building models that integrate our knowledge and understanding of surgery remains a challenging endeavor. In this paper, we propose a novel way to integrate conceptual knowledge into temporal analysis tasks using temporal concept graph networks. In the proposed networks, a knowledge graph is incorporated into the temporal video analysis of surgical notions, learning the meaning of concepts and relations as they apply to the data. We demonstrate results in surgical video data for tasks such as verification of the critical view of safety, estimation of the Parkland grading scale as well as recognizing instrument-action-tissue triplets. 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.

SAGES video acquisition framework-analysis of available OR recording technologies by the SAGES AI task force.

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 the OR staff, kinematic measurements (motion of surgical instruments), and recording of the endoscopic video streams. Additional analysis through computer vision (CV), which allows software to understand and perform predictive tasks on images, can allow for automatic phase segmentation, instrument tracking, and derivative performance-geared metrics. With this survey, we summarize available surgical video acquisition technologies and associated performance analysis platforms. METHODS: In an effort promoted by the SAGES Artificial Intelligence Task Force, we surveyed the available video recording technology companies. Of thirteen companies approached, nine were interviewed, each over an hour-long video conference. A standard set of 17 questions was administered. Questions spanned from data acquisition capacity, quality, and synchronization of video with other data, availability of analytic tools, privacy, and access. RESULTS: Most platforms (89%) store video in full-HD (1080p) resolution at a frame rate of 30 fps. Most (67%) of available platforms store data in a Cloud-based databank as opposed to institutional hard drives. CV powered analysis is featured in some platforms: phase segmentation in 44% platforms, out of body blurring or tool tracking in 33%, and suture time in 11%. Kinematic data are provided by 22% and perfusion imaging in one device. CONCLUSION: Video acquisition platforms on the market allow for in depth performance analysis through manual and automated review. Most of these devices will be integrated in upcoming robotic surgical platforms. Platform analytic supplementation, including CV, may allow for more refined performance analysis to surgeons and trainees. Most current AI features are related to phase segmentation, instrument tracking, and video blurring.

Multicentric exploration of tool annotation in robotic surgery: lessons learned when starting a surgical artificial intelligence project.

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.

ADAMTSL2 protein and a soluble biomarker signature identify at-risk non-alcoholic steatohepatitis and fibrosis in adults with NAFLD.

BACKGROUND & AIMS: Identifying fibrosis in non-alcoholic fatty liver disease (NAFLD) is essential to predict liver-related outcomes and guide treatment decisions. A protein-based signature of fibrosis could serve as a valuable, non-invasive diagnostic tool. This study sought to identify circulating proteins associated with fibrosis in NAFLD. METHODS: We used aptamer-based proteomics to measure 4,783 proteins in 2 cohorts (Cohort A and B). Targeted, quantitative assays coupling aptamer-based protein pull down and mass spectrometry (SPMS) validated the profiling results in a bariatric and NAFLD cohort (Cohort C and D, respectively). Generalized linear modeling-logistic regression assessed the ability of candidate proteins to classify fibrosis. RESULTS: From the multiplex profiling, 16 proteins differed significantly by fibrosis in cohorts A (n = 62) and B (n = 98). Quantitative and robust SPMS assays were developed for 8 proteins and validated in Cohorts C (n = 71) and D (n = 84). The A disintegrin and metalloproteinase with thrombospondin motifs like 2 (ADAMTSL2) protein accurately distinguished non-alcoholic fatty liver (NAFL)/non-alcoholic steatohepatitis (NASH) with fibrosis stage 0-1 (F0-1) from at-risk NASH with fibrosis stage 2-4, with AUROCs of 0.83 and 0.86 in Cohorts C and D, respectively, and from NASH with significant fibrosis (F2-3), with AUROCs of 0.80 and 0.83 in Cohorts C and D, respectively. An 8-protein panel distinguished NAFL/NASH F0-1 from at-risk NASH (AUROCs 0.90 and 0.87 in Cohort C and D, respectively) and NASH F2-3 (AUROCs 0.89 and 0.83 in Cohorts C and D, respectively). The 8-protein panel and ADAMTSL2 protein had superior performance to the NAFLD fibrosis score and fibrosis-4 score. CONCLUSION: The ADAMTSL2 protein and an 8-protein soluble biomarker panel are highly associated with at-risk NASH and significant fibrosis; they exhibited superior diagnostic performance compared to standard of care fibrosis scores. LAY SUMMARY: Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of liver disease worldwide. Diagnosing NAFLD and identifying fibrosis (scarring of the liver) currently requires a liver biopsy. Our study identified novel proteins found in the blood which may identify fibrosis without the need for a liver biopsy.

Robotic-assisted minimally invasive esophagectomy (RAMIE) for esophageal cancer training curriculum-a worldwide Delphi consensus study.

BACKGROUND: Structured training protocols can safely improve skills prior initiating complex surgical procedures such as robotic-assisted minimally invasive esophagectomy (RAMIE). As no consensus on a training curriculum for RAMIE has been established so far it is our aim to define a protocol for RAMIE with the Delphi consensus methodology. METHODS: Fourteen worldwide RAMIE experts were defined and were enrolled in this Delphi consensus project. An expert panel was created and three Delphi rounds were performed starting December 2019. Items required for RAMIE included, but were not limited to, virtual reality simulation, wet-lab training, proctoring, and continued monitoring and education. After rating performed by the experts, consensus was defined when a Cronbach alpha of ≥0.80 was reached. If ≥80% of the committee reached a consensus an item was seen as fundamental. RESULTS: All Delphi rounds were completed by 12-14 (86-100%) participants. After three rounds analyzing our 49-item questionnaire, 40 items reached consensus for a training curriculum of RAMIE. CONCLUSION: The core principles for RAMIE training were defined. This curriculum may lead to a wider adoption of RAMIE and a reduction in time to reach proficiency.

Surgical data science - from concepts toward clinical translation.

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.

SAGES consensus recommendations on an annotation framework for surgical video.

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. METHODS: Four working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes: (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a consensus survey based on suggested recommendations from each of the working groups. RESULTS: After three Delphi rounds, consensus was reached on recommendations for annotation within each of these domains. A hierarchy for annotation of temporal events in surgery was established. CONCLUSIONS: While additional work remains to achieve accepted standards for video annotation in surgery, the consensus recommendations on a general framework for annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration.

Challenges in surgical video annotation.

Annotation of surgical video is important for establishing ground truth in surgical data science endeavors that involve computer vision. With the growth of the field over the last decade, several challenges have been identified in annotating spatial, temporal, and clinical elements of surgical video as well as challenges in selecting annotators. In reviewing current challenges, we provide suggestions on opportunities for improvement and possible next steps to enable translation of surgical data science efforts in surgical video analysis to clinical research and practice.

Automated operative phase identification in peroral endoscopic myotomy.

BACKGROUND: Artificial intelligence (AI) and computer vision (CV) have revolutionized image analysis. In surgery, CV applications have focused on surgical phase identification in laparoscopic videos. We proposed to apply CV techniques to identify phases in an endoscopic procedure, peroral endoscopic myotomy (POEM). METHODS: POEM videos were collected from Massachusetts General and Showa University Koto Toyosu Hospitals. Videos were labeled by surgeons with the following ground truth phases: (1) Submucosal injection, (2) Mucosotomy, (3) Submucosal tunnel, (4) Myotomy, and (5) Mucosotomy closure. The deep-learning CV model-Convolutional Neural Network (CNN) plus Long Short-Term Memory (LSTM)-was trained on 30 videos to create POEMNet. We then used POEMNet to identify operative phases in the remaining 20 videos. The model's performance was compared to surgeon annotated ground truth. RESULTS: POEMNet's overall phase identification accuracy was 87.6% (95% CI 87.4-87.9%). When evaluated on a per-phase basis, the model performed well, with mean unweighted and prevalence-weighted F1 scores of 0.766 and 0.875, respectively. The model performed best with longer phases, with 70.6% accuracy for phases that had a duration under 5 min and 88.3% accuracy for longer phases. DISCUSSION: A deep-learning-based approach to CV, previously successful in laparoscopic video phase identification, translates well to endoscopic procedures. With continued refinements, AI could contribute to intra-operative decision-support systems and post-operative risk prediction.

Computer vision in surgery.

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.

Desmoid tumor presenting 2 years after elective Roux-en-Y gastric bypass: a case report and review of the literature.

Desmoid tumors are rare malignancies derived from myofibroblasts, which can cause significant morbidity due to life-threatening invasion of local structures. Risk factors include familial adenomatous polyposis, antecedent surgical trauma and estrogen exposure. We described a previously healthy 27-year-old female in whom a desmoid tumor developed 2 years after a Roux-en-Y gastric bypass was performed for the treatment of obesity. Computed tomography scan demonstrated a 16-cm complex density intra-abdominal mass. Exploratory laparotomy was performed, revealing a mass firmly adherent to the Roux limb, as well as the jejunojejunostomy and distal portion of the bilopancreatic limb. En bloc resection of the mass and the Roux limb was required, as well as reconstruction of the Roux-en-Y anatomy. This case describes a unique, long-term complication of bariatric surgery, in light of a growing population of patients with altered gastric anatomy.

Computer Vision Analysis of Intraoperative Video: Automated Recognition of Operative Steps in Laparoscopic Sleeve Gastrectomy.

OBJECTIVE(S): To develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG). BACKGROUND: Computer vision, a form of artificial intelligence (AI), allows for quantitative analysis of video by computers for identification of objects and patterns, such as in autonomous driving. METHODS: Intraoperative video from LSG from an academic institution was annotated by 2 fellowship-trained, board-certified bariatric surgeons. Videos were segmented into the following steps: 1) port placement, 2) liver retraction, 3) liver biopsy, 4) gastrocolic ligament dissection, 5) stapling of the stomach, 6) bagging specimen, and 7) final inspection of staple line. Deep neural networks were used to analyze videos. Accuracy of operative step identification by the AI was determined by comparing to surgeon annotations. RESULTS: Eighty-eight cases of LSG were analyzed. A random 70% sample of these clips was used to train the AI and 30% to test the AI's performance. Mean concordance correlation coefficient for human annotators was 0.862, suggesting excellent agreement. Mean (±SD) accuracy of the AI in identifying operative steps in the test set was 82% ±â€Š4% with a maximum of 85.6%. CONCLUSIONS: AI can extract quantitative surgical data from video with 85.6% accuracy. This suggests operative video could be used as a quantitative data source for research in intraoperative clinical decision support, risk prediction, or outcomes studies.

Risk factors and prevalence of liver disease in review of 2557 routine liver biopsies performed during bariatric surgery.

BACKGROUND: Obesity is a known risk factor for nonalcoholic fatty liver disease (NAFLD). However, among individuals undergoing bariatric surgery, the prevalence and risk factors for NAFLD, as well as distinct phenotypes of steatosis, nonalcoholic steatohepatitis (NASH), and fibrosis remain incompletely understood. OBJECTIVES: To determine the prevalence and risk factors for steatosis, NASH, and fibrosis in individuals undergoing routine bariatric surgery. SETTING: Academic medical center in the United States. METHODS: Liver wedge biopsies were performed at the time of surgery between 2001 and 2017. Pathology reports were reviewed, and individuals were grouped by NAFLD phenotype. Covariates including demographic characteristics, co-morbidities, and preoperative laboratory values were compared between groups using Student's t test, Pearson's χ2, and logistic regression. RESULTS: Liver biopsies were obtained in 97.7% of first-time bariatric procedures, representing 2557 patients. Mean age was 45.6 years, mean body mass index was 46.7, and most were non-Hispanic white (76.1%) and female (71.6%). On histologic review 61.2% had steatosis and 30.9% NASH. Fibrosis was identified in 29.3% of individuals, and 7.8% had stage ≥2 fibrosis. On logistic regression, elevated aspartate aminotransferase (odds ratio [OR] 1.87; P < .001) and elevated alanine aminotransferase (OR 1.62; P < .001) were independently associated with fibrosis. Elevated hemoglobin A1C of 5.7% to 6.5% (OR 1.29; P < .01) and >6.5% (OR 3.23; P < .001) were also associated with fibrosis. A similar trend was seen for NASH. CONCLUSIONS: NASH and/or fibrosis is present in nearly one third of patients undergoing routine bariatric surgery. Risk factors include diabetes, elevated liver enzymes, and diabetes. Risk assessment and aggressive screening should be considered in patients undergoing bariatric surgery.

Artificial Intelligence in Surgery: Promises and Perils.

OBJECTIVE: The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments. SUMMARY BACKGROUND DATA: AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers. METHODS: A review of AI papers across computer science, statistics, and medical sources was conducted to identify key concepts and techniques within AI that are driving innovation across industries, including surgery. Limitations and challenges of working with AI were also reviewed. RESULTS: Four main subfields of AI were defined: (1) machine learning, (2) artificial neural networks, (3) natural language processing, and (4) computer vision. Their current and future applications to surgical practice were introduced, including big data analytics and clinical decision support systems. The implications of AI for surgeons and the role of surgeons in advancing the technology to optimize clinical effectiveness were discussed. CONCLUSIONS: Surgeons are well positioned to help integrate AI into modern practice. Surgeons should partner with data scientists to capture data across phases of care and to provide clinical context, for AI has the potential to revolutionize the way surgery is taught and practiced with the promise of a future optimized for the highest quality patient care.