Use of artificial intelligence for decision-support to avoid high-risk behaviors during laparoscopic cholecystectomy.

INTRODUCTION: Bile duct injuries (BDIs) are a significant source of morbidity among patients undergoing laparoscopic cholecystectomy (LC). GoNoGoNet is an artificial intelligence (AI) algorithm that has been developed and validated to identify safe ("Go") and dangerous ("No-Go") zones of dissection during LC, with the potential to prevent BDIs through real-time intraoperative decision-support. This study evaluates GoNoGoNet's ability to predict Go/No-Go zones during LCs with BDIs. METHODS AND PROCEDURES: Eleven LC videos with BDI (BDI group) were annotated by GoNoGoNet. All tool-tissue interactions, including the one that caused the BDI, were characterized in relation to the algorithm's predicted location of Go/No-Go zones. These were compared to another 11 LC videos with cholecystitis (control group) deemed to represent "safe cholecystectomy" by experts. The probability threshold of GoNoGoNet annotations were then modulated to determine its relationship to Go/No-Go predictions. Data is shown as % difference [99% confidence interval]. RESULTS: Compared to control, the BDI group showed significantly greater proportion of sharp dissection (+ 23.5% [20.0-27.0]), blunt dissection (+ 32.1% [27.2-37.0]), and total interactions (+ 33.6% [31.0-36.2]) outside of the Go zone. Among injury-causing interactions, 4 (36%) were in the No-Go zone, 2 (18%) were in the Go zone, and 5 (45%) were outside both zones, after maximizing the probability threshold of the Go algorithm. CONCLUSION: AI has potential to detect unsafe dissection and prevent BDIs through real-time intraoperative decision-support. More work is needed to determine how to optimize integration of this technology into the operating room workflow and adoption by end-users.

What is the educational value and clinical utility of artificial intelligence for intraoperative and postoperative video analysis? A survey of surgeons and trainees.

INTRODUCTION: Surgical complications often occur due to lapses in judgment and decision-making. Advances in artificial intelligence (AI) have made it possible to train algorithms that identify anatomy and interpret the surgical field. These algorithms can potentially be used for intraoperative decision-support and postoperative video analysis and feedback. Despite the very early success of proof-of-concept algorithms, it remains unknown whether this innovation meets the needs of end-users or how best to deploy it. This study explores users' opinion on the value, usability and design for adapting AI in operating rooms. METHODS: A device-agnostic web-accessible software was developed to provide AI inference either (1) intraoperatively on a live video stream (synchronous mode), or (2) on an uploaded video or image file (asynchronous mode) postoperatively for feedback. A validated AI model (GoNoGoNet), which identifies safe and dangerous zones of dissection during laparoscopic cholecystectomy, was used as the use case. Surgeons and trainees performing laparoscopic cholecystectomy interacted with the AI platform and completed a 5-point Likert scale survey to evaluate the educational value, usability and design of the platform. RESULTS: Twenty participants (11 surgeons and 9 trainees) evaluated the platform intraoperatively (n = 10) and postoperatively (n = 11). The majority agreed or strongly agreed that AI is an effective adjunct to surgical training (81%; neutral = 10%), effective for providing real-time feedback (70%; neutral = 20%), postoperative feedback (73%; neutral = 27%), and capable of improving surgeon confidence (67%; neutral = 29%). Only 40% (neutral = 50%) and 57% (neutral = 43%) believe that the tool is effective in improving intraoperative decisions and performance, or beneficial for patient care, respectively. Overall, 38% (neutral = 43%) reported they would use this platform consistently if available. The majority agreed or strongly agreed that the platform was easy to use (81%; neutral = 14%) and has acceptable resolution (62%; neutral = 24%), while 30% (neutral = 20%) reported that it disrupted the OR workflow, and 20% (neutral = 0%) reported significant time lag. All respondents reported that such a system should be available "on-demand" to turn on/off at their discretion. CONCLUSIONS: Most found AI to be a useful tool for providing support and feedback to surgeons, despite several implementation obstacles. The study findings will inform the future design and usability of this technology in order to optimize its clinical impact and adoption by end-users.

Validation of an artificial intelligence platform for the guidance of safe laparoscopic cholecystectomy.

BACKGROUND: Many surgical adverse events, such as bile duct injuries during laparoscopic cholecystectomy (LC), occur due to errors in visual perception and judgment. Artificial intelligence (AI) can potentially improve the quality and safety of surgery, such as through real-time intraoperative decision support. GoNoGoNet is a novel AI model capable of identifying safe ("Go") and dangerous ("No-Go") zones of dissection on surgical videos of LC. Yet, it is unknown how GoNoGoNet performs in comparison to expert surgeons. This study aims to evaluate the GoNoGoNet's ability to identify Go and No-Go zones compared to an external panel of expert surgeons. METHODS: A panel of high-volume surgeons from the SAGES Safe Cholecystectomy Task Force was recruited to draw free-hand annotations on frames of prospectively collected videos of LC to identify the Go and No-Go zones. Expert consensus on the location of Go and No-Go zones was established using Visual Concordance Test pixel agreement. Identification of Go and No-Go zones by GoNoGoNet was compared to expert-derived consensus using mean F1 Dice Score, and pixel accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 47 frames from 25 LC videos, procured from 3 countries and 9 surgeons, were annotated simultaneously by an expert panel of 6 surgeons and GoNoGoNet. Mean (± standard deviation) F1 Dice score were 0.58 (0.22) and 0.80 (0.12) for Go and No-Go zones, respectively. Mean (± standard deviation) accuracy, sensitivity, specificity, PPV and NPV for the Go zones were 0.92 (0.05), 0.52 (0.24), 0.97 (0.03), 0.70 (0.21), and 0.94 (0.04) respectively. For No-Go zones, these metrics were 0.92 (0.05), 0.80 (0.17), 0.95 (0.04), 0.84 (0.13) and 0.95 (0.05), respectively. CONCLUSIONS: AI can be used to identify safe and dangerous zones of dissection within the surgical field, with high specificity/PPV for Go zones and high sensitivity/NPV for No-Go zones. Overall, model prediction was better for No-Go zones compared to Go zones. This technology may eventually be used to provide real-time guidance and minimize the risk of adverse events.

Weight loss outcomes for patients undergoing conversion to Roux-en-Y-gastric bypass after sleeve gastrectomy.

BACKGROUND: Despite excellent reported outcomes after laparoscopic sleeve gastrectomy (LSG), a percentage of patients go on to have a secondary bariatric surgery to manage side-effects or address weight regain after LSG. Reported weight loss outcomes for patients undergoing laparoscopic Roux-en-Y gastric bypass (LRYGB) after previous LSG are variable. We sought to determine the weight-loss outcomes of patients undergoing LRYGB after LSG in the largest bariatric surgical network in Canada and to determine whether outcomes differ according to indications for conversion. METHODS: The Bariatric Registry is a multi-center database with prospectively collected standardized data on patients undergoing bariatric surgery at ten Bariatric Centers of Excellence within the Ontario Bariatric Network in Ontario, Canada. A retrospective analysis was performed of patients who underwent LRYGB after previous LSG between 2012 and 2019. Weight loss outcomes were compared between patients who underwent LRYGB for insufficient weight loss/weight regain and those who underwent conversion to LRYGB for other reasons. RESULTS: Excluding patients with multiple revisions and those without follow-up data, 48 patients were included in the analysis: 33 patients (69%) underwent conversion to LRGYB for insufficient weight loss/weight regain (Group 1) and 15 patients (31%) underwent conversion for other reasons (Group 2). Mean body mass index (BMI) measured pre-LSG, pre-LRYGB, and at mid-term follow-up after LRYGB was 61, 48, and 43 kg/m2 in Group 1 and 51, 39, and 34 kg/m2 in Group 2, respectively. ΔBMI and %total weight loss (TWL) at mid-term follow-up were not significantly different between the groups. CONCLUSIONS: Conversion to LRYGB after previous LSG resulted in an additional loss of 4 kg/m2 in BMI points at mid-term follow-up. Patients lost a similar number of BMI points and cumulative %TWL was similar regardless of reason for conversion. This can help inform surgical decision-making in the setting of weight regain after LSG.

Optimizing postoperative care protocols in thoracic surgery: best evidence and new technology.

Postoperative clinical pathways have been shown to improve postoperative care and decrease length of stay in hospital. In thoracic surgery there is a need to develop chest tube management pathways. This paper considers four aspects of chest tube management: (I) appraising the role of chest X-rays in the management of lung resection patients with chest drains; (II) selecting of a fluid output threshold below which chest tubes can be removed safely; (III) deciding whether suction should be applied to chest tubes; (IV) and selecting the safest method for chest tube removal. There is evidence that routine use of chest X-rays does not influence the management of chest tubes. There is a lack of consensus on the highest fluid output threshold below which chest tubes can be safely removed. The optimal use of negative intra-pleural pressure has not yet been established despite multiple randomized controlled trials and meta-analyses. When attempting to improve efficiency in the management of chest tubes, evidence in support of drain removal without a trial of water seal should be considered. Inconsistencies in the interpretation of air leaks and in chest tube management are likely contributors to the conflicting results found in the literature. New digital pleural drainage systems, which provide a more objective air leak assessment and can record air leak trend over time, will likely contribute to the development of new evidence-based guidelines. Technology should be combined with continued efforts to standardize care, create clinical pathways, and analyze their impact on postoperative outcomes.