AI-powered real-time annotations during urologic surgery: The future of training and quality metrics

Laura Zuluaga, Jordan Miller Rich, Raghav Gupta, Adriana Pedraza, Burak Ucpinar, Kennedy E. Okhawere, Indu Saini, Priyanka Dwivedi, Dhruti Patel, Osama Zaytoun, Mani Menon, Ashutosh Tewari, Ketan K. Badani

Research output: Contribution to journalReview articlepeer-review

Abstract

Introduction and objective: Real-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for surgical education and qualitative initiatives. We demonstrate the first use of AI in urologic robotic surgery to capture live surgical video and annotate key surgical steps and safety milestones in real-time. Summary background data: While AI models possess the capability to generate automated annotations based on a collection of video images, the real-time implementation of such technology in urological robotic surgery to aid surgeon and training staff it is still pending to be studied. Methods: We conducted an educational symposium, which broadcasted 2 live procedures, a robotic-assisted radical prostatectomy (RARP) and a robotic-assisted partial nephrectomy (RAPN). A surgical AI platform system (Theator, Palo Alto, CA) generated real-time annotations and identified operative safety milestones. This was achieved through trained algorithms, conventional video recognition, and novel Video Transfer Network technology which captures clips in full context, enabling automatic recognition and surgical mapping in real-time. Results: Real-time AI annotations for procedure #1, RARP, are found in Table 1. The safety milestone annotations included the apical safety maneuver and deliberate views of structures such as the external iliac vessels and the obturator nerve. Real-time AI annotations for procedure #2, RAPN, are found in Table 1. Safety milestones included deliberate views of structures such as the gonadal vessels and the ureter. AI annotated surgical events included intraoperative ultrasound, temporary clip application and removal, hemostatic powder application, and notable hemorrhage. Conclusions: For the first time, surgical intelligence successfully showcased real-time AI annotations of 2 separate urologic robotic procedures during a live telecast. These annotations may provide the technological framework for send automatic notifications to clinical or operational stakeholders. This technology is a first step in real-time intraoperative decision support, leveraging big data to improve the quality of surgical care, potentially improve surgical outcomes, and support training and education.

Original languageEnglish
Pages (from-to)57-66
Number of pages10
JournalUrologic Oncology: Seminars and Original Investigations
Volume42
Issue number3
DOIs
StatePublished - Mar 2024

Keywords

  • Artificial intelligence
  • Robotics
  • Surgical steps

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