Computer vision in laparoscopic gastrointestinal surgery: A review of anatomical recognition and clinical navigation

Abstract

Laparoscopic surgery is constrained by a two-dimensional visual field and the absence of tactile feedback, which pose challenges for the precise intraoperative identification of critical anatomical structures. Recently, rapid progress in computer vision (CV) has offered a new way to address this clinical limitation. This review provides a narrative overview of the latest progress in CV in laparoscopic gastrointestinal surgery, ranging from fundamental temporal surgical phase recognition and visual quality preprocessing to high-precision dynamic segmentation and real-time bleeding warning of critically high-risk tubular structures such as mesenteric vessels, ureters, and the biliary tract. The review also examines how CV is being combined with augmented reality, three dimensional (3D) registration, and multimodal imaging, illustrating the shift in minimally invasive surgery from two dimensional image analysis toward 3D clinical navigation. Although CV technologies have demonstrated promising performance in several core tasks, large-scale clinical translation still faces challenges, including limited algorithmic generalization, reliance on single-center datasets, and a paucity of large-scale real-world evidence-based validation. Bolstered by the establishment of multicenter standardized datasets, introduction of edge computing, refinement of data privacy regulations, and strengthening of ethical oversight, CV may help advance gastrointestinal surgery from passive visual assistance toward more proactive intraoperative support and potentially toward greater intraoperative autonomy.

Keywords: Computer vision; Deep learning; Laparoscopic surgery; Anatomic landmarks; Surgical navigation; Augmented reality

Core Tip: Computer vision is advancing laparoscopic gastrointestinal surgery from two dimensional image recognition toward real-time anatomical navigation and context-aware assistance. This review summarizes progress in surgical phase recognition, visual enhancement, critical-structure identification, three dimensional and augmented-reality navigation, and robotic integration, while emphasizing the gap between technical performance and demonstrated clinical benefit. Key priorities include multicenter validation, procedure-specific datasets, privacy-preserving learning, human-artificial intelligence collaboration, and prospective evaluation of surgeon-, procedure-, and patient-centered outcomes. Semi-autonomous functions should remain under surgeon oversight.