Automated artificial intelligence-based phase-recognition system for esophageal endoscopic submucosal dissection (with video).

BACKGROUND AND AIMS: Endoscopic submucosal dissection (ESD) for superficial esophageal cancer is a multistep treatment involving several endoscopic processes. Although analyzing each phase separately is worthwhile, it is not realistic in practice owing to the need for considerable manpower. To solve this problem, we aimed to establish a state-of-the-art artificial intelligence (AI)-based system, specifically, an automated phase-recognition system that can automatically identify each endoscopic phase based on video images. METHODS: Ninety-four videos of ESD procedures for superficial esophageal cancer were evaluated in this single-center study. A deep neural network-based phase-recognition system was developed in an automated manner to recognize each of the endoscopic phases. The system was trained with the use of videos that were annotated and verified by 2 GI endoscopists. RESULTS: The overall accuracy of the AI model for automated phase recognition was 90%, and the average precision, recall, and F value rates were 91%, 90%, and 90%, respectively. Two representative ESD videos predicted by the model indicated the usability of AI in clinical practice. CONCLUSIONS: We demonstrated that an AI-based automated phase-recognition system for esophageal ESD can be established with high accuracy. To the best of our knowledge, this is the first report on automated recognition of ESD treatment phases. Because this system enabled a detailed analysis of phases, collecting large volumes of data in the future may help to identify quality indicators for treatment techniques and uncover unmet medical needs that necessitate the creation of new treatment methods and devices.

The relationship between the esophageal endoscopic submucosal dissection technical difficulty and its intraoperative process.

BACKGROUND: Estimating the esophageal endoscopic submucosal dissection (ESD) technical difficulty is important to reduce complications. Endoscopic duration is one of the related factors to a technical difficulty. The relationship between the esophageal ESD technical difficulty and its intraoperative process was analyzed as a first step toward automatic technical difficulty recognition using artificial intelligence. METHODS: This study enrolled 75 patients with superficial esophageal cancer who underwent esophageal ESD. The technical difficulty score was established, which consisted of three factors, including total procedure duration, en bloc resection, and complications. Additionally, technical difficulty-related factors, which were perioperative factors that included the intraoperative process, were investigated. RESULTS: Eight (11%) patients were allocated to high difficulty, whereas 67 patients (89%) were allocated to low difficulty. The intraoperative process, which was shown as the extension of each endoscopic phase, was significantly related to a technical difficulty. The area under the curve (AUC) values were higher at all the phase duration than at the clinical characteristics. Submucosal dissection phase (AUC 0.902; 95% confidence intervals (CI) 0.752-1.000), marking phase (AUC 0.827; 95% CI 0.703-0.951), and early phase which was defined as the duration from the start of marking to the end of submucosal injection (AUC 0.847; 95% CI 0.701-0.992) were significantly related to technical difficulty. CONCLUSIONS: The intraoperative process, particularly early phase, was strongly associated with esophageal ESD technical difficulty. This study demonstrated the potential for automatic evaluation of esophageal ESD technical difficulty when combined with an AI-based automatic phase evaluation system.

Evaluating surgical expertise with AI-based automated instrument recognition for robotic distal gastrectomy.

Introduction: Complexities of robotic distal gastrectomy (RDG) give reason to assess physician's surgical skill. Varying levels in surgical skill affect patient outcomes. We aim to investigate how a novel artificial intelligence (AI) model can be used to evaluate surgical skill in RDG by recognizing surgical instruments. Methods: Fifty-five consecutive robotic surgical videos of RDG for gastric cancer were analyzed. We used Deeplab, a multi-stage temporal convolutional network, and it trained on 1234 manually annotated images. The model was then tested on 149 annotated images for accuracy. Deep learning metrics such as Intersection over Union (IoU) and accuracy were assessed, and the comparison between experienced and non-experienced surgeons based on usage of instruments during infrapyloric lymph node dissection was performed. Results: We annotated 540 Cadiere forceps, 898 Fenestrated bipolars, 359 Suction tubes, 307 Maryland bipolars, 688 Harmonic scalpels, 400 Staplers, and 59 Large clips. The average IoU and accuracy were 0.82 ± 0.12 and 87.2 ± 11.9% respectively. Moreover, the percentage of each instrument's usage to overall infrapyloric lymphadenectomy duration predicted by AI were compared. The use of Stapler and Large clip were significantly shorter in the experienced group compared to the non-experienced group. Conclusions: This study is the first to report that surgical skill can be successfully and accurately determined by an AI model for RDG. Our AI gives us a way to recognize and automatically generate instance segmentation of the surgical instruments present in this procedure. Use of this technology allows unbiased, more accessible RDG surgical skill.

Effect of aqueous olanexidine versus alcohol-based chlorhexidine for surgical skin antisepsis on incidence of surgical site infections in gastrointestinal surgery: multicentre randomised controlled clinical trial (OEDO trial) protocol.

INTRODUCTION: Surgical site infections (SSIs) are among the most common nosocomial infections in surgery patients. Two types of preparations, povidone-iodine and chlorhexidine-alcohol, are commonly used in preoperative antiseptic procedures worldwide. However, there are inconsistencies among international guideline recommendations concerning skin antiseptics. This trial aimed to evaluate the superiority of olanexidine, which reduced SSI rates more than povidone-iodine in our previous randomised trial, over chlorhexidine-alcohol in clean-contaminated surgery. METHODS AND ANALYSIS: This multicentre randomised controlled clinical trial will compare two antiseptics (1.5% olanexidine and 1.0% chlorhexidine-alcohol) to prevent SSI in clean-contaminated gastrointestinal surgeries with surgical wounds. On providing consent, patients aged <18 years will be included. The primary outcome will be the postoperative 30-day overall SSI rate, while the secondary outcomes will be the postoperative 30-day superficial incisional SSI rate, deep incisional SSI rate, organ/space SSI rate, positive bacterial wound culture rate, cultured bacterial strains, rates of intervention-related toxicity and allergic events (eg, erythema, pruritus, dermatitis and other symptoms of allergy around the region disinfected by the antiseptic during surgery), rate of reoperations due to SSI, medical economic effect indicators (based on health insurance claims) and hospital duration. The Mantel-Haenszel method will be used to estimate the adjusted risk ratio and its 95% CI for the primary analysis, which will compare the treatment effects. ETHICS AND DISSEMINATION: The protocol was approved by the Institutional Review Board of Keio University School of Medicine and subsequently by the board of each participating site. Participant recruitment began in January 2023. The final results will be published in medical journals after international peer review. TRIAL REGISTRATION NUMBER: UMIN000049712.