Exploring the impact of an artificial intelligence-based intraoperative image navigation system in laparoscopic surgery on clinical outcomes: A protocol for a multicenter randomized controlled trial

Abstract

Background In the research field of artificial intelligence (AI) in surgery, there are many open questions that must be clarified. Well-designed randomized controlled trials (RCTs) are required to explore the positive clinical impacts by comparing the use and non-use of AI-based intraoperative image navigation. Therefore, herein, we propose the ImNavi trial, a multicenter RCT, to compare the use and non-use of an AI-based intraoperative image navigation system in laparoscopic surgery.<br>Methods The ImNavi trial is a Japanese multicenter RCT involving 1:1 randomization between the use and non-use of an AI-based intraoperative image navigation system in laparoscopic colorectal surgery. The participating institutions will include three high-volume centers with sufficient laparoscopic colorectal surgery caseloads (>100 cases/year), including one national cancer center and two university hospitals in Japan. Written informed consent will be obtained from all patients. Patients aged between 18 and 80 years scheduled to undergo laparoscopic left-sided colorectal resection will be included in the study. The primary outcome is the time required for each target organ, including the ureter and autonomic nerves, to be recognized by the surgeon after its initial appearance on the monitor. Secondary outcomes include intraoperative target organ injuries, intraoperative complications, operation time, blood loss, duration of postoperative hospital stay, postoperative complications within 30 days, postoperative male sexual dysfunction 1 month after surgery, surgeon′s confidence in recognizing each target organ, and the postoperative fatigue of the primary surgeon.<br>Discussion The impact of AI-based surgical applications on clinical outcomes beyond numerical expression will be explored from a variety of viewpoints while evaluating quantitative items, including intraoperative complications and operation time, as secondary endpoints. We expect that the findings of this RCT will contribute to advancing research in the domain of AI in surgery.