Artificial-intelligence assistance for diagnosis, detection and surgical decision-making in colorectal peritoneal metastases: A cross-sectional BIG-RENAPE pilot study.

BACKGROUND: Human visual performance in diagnosing and detecting peritoneal metastases (PM) during laparoscopy remains limited. A deep-learning algorithm was developed to assist PM detection on laparoscopic videos. This study evaluated its impact on diagnostic accuracy, detection capability, and therapeutic decision-making. METHODS: Laparoscopic video sequences showing 14 nodules and 20 selected peritoneal regions were randomly extracted from a large dataset. An online survey was distributed to digestive surgeons specialized in oncologic surgery, either experts in PM management (BIGRENAPE) or non-experts (non-BIGRENAPE). For each nodule, surgeons assessed malignancy (benign vs malignant) and selected a management strategy: biopsy vs no biopsy for non-BIGRENAPE, and resection/destruction, frozen-section biopsy, or no action for BIGRENAPE, first without then with AI assistance. For each region, surgeons also estimated the number of malignant nodules with and without AI assistance. RESULTS: Sixty-nine surgeons participated (44 non-BIGRENAPE, 25 BIGRENAPE). Diagnostic accuracy with AI assistance remained stable, from 63% to 61% in non-BIGRENAPE and from 70% to 71% in BIGRENAPE experts. Among non-BIGRENAPE surgeons, correct decision-making remained stable from 67% to 70%. In contrast, AI significantly enhanced appropriate surgical decision-making among BIGRENAPE experts from 65% to 69% (p = 0.03; OR 2.27, 95% CI 1.08-5.11) and modified therapeutic strategy in 20.9%. Detection performance showed a moderate correlation between true and estimated malignant nodule counts (Spearman 0.72-0.83), with slight deterioration under AI assistance. CONCLUSIONS: Human performance in diagnosing, detecting, and managing colorectal PM remains suboptimal. AI assistance improved expert surgical decision-making but not diagnostic or quantification performance.