Histopathological proof-of-concept validation of AI-assisted peripheral neural track recognition during laparoscopic...

Nerve injury remains a major cause of functional impairment after gastrointestinal surgery. The EUREKA® artificial intelligence (AI) system enabled real-time intraoperative recognition of anatomical structures including nerves. However, histopathological validation of AI-predicted nerve structures remains lacking. The aim is to provide histopathological validation of AI-assisted peripheral neural track recognition during gastrointestinal surgery.

A prospective observational study was conducted in 23 patients undergoing laparoscopic gastrointestinal procedures. Targeted biopsies were selectively obtained from structures identified by the EUREKA® system as potential nerve tissue within the extracted surgical specimen. All samples underwent histopathological evaluation. Biopsies were intentionally restricted to peripheral nerves contained within the specimen, representing a challenging validation model, excluding major autonomic nerves and plexuses.

The surgical procedures included eight right hemicolectomies with complete mesocolic excision (34.8%), seven left hemicolectomies (30.4%), three conventional right hemicolectomies (13%), two anterior resections of the rectum (8.7%), one splenic flexure resection (4.3%), one subtotal gastrectomy (4.3%), and one total gastrectomy (4.3%). Histopathological analysis of 32 targeted biopsies confirmed peripheral nerve tissue in twelve biopsies (37.5%) obtained from very small peripheral neural structures.

This is the first histopathological validation of anatomical recognition performed by an AI system during minimally invasive gastrointestinal surgery. Although histopathological confirmation was achieved in more than one-third of targeted biopsies obtained from very small peripheral neural structures, the study was intentionally designed to evaluate the most challenging subset of neural anatomy rather than major autonomic nerves and plexuses. These findings support the feasibility of AI-assisted surgical navigation for identifying potential anatomical "tracks" during dissection. However, AI should be considered an adjunct rather than a substitute for surgical expertise, as the surgeon must interpret these signals according to anatomical location and context to support nerve-preserving strategies. Further studies are required to improve nerve recognition and evaluate the clinical impact of AI-guided nerve recognition.