For several cancer patients, operative resection with curative intent can end
up in early recurrence of the cancer. Current limitations in peri-operative
cancer staging and especially intra-operative misidentification of visible
metastases is likely the main reason leading to unnecessary operative
interventions in the affected individuals. Here, we evaluate whether an
artificial intelligence (AI) system can improve recognition of peritoneal
surface metastases on routine staging laparoscopy images from patients with
gastrointestinal malignancies. In a simulated setting evaluating biopsied
peritoneal lesions, a prototype deep learning surgical guidance system
outperformed oncologic surgeons in identifying peritoneal surface metastases.
In this environment the developed AI model would have improved the
identification of metastases by 5% while reducing the number of unnecessary
biopsies by 28% compared to current standard practice. Evaluating non-biopsied
peritoneal lesions, the findings support the possibility that the AI system
could identify peritoneal surface metastases that were falsely deemed benign in
clinical practice. Our findings demonstrate the technical feasibility of an AI
system for intra-operative identification of peritoneal surface metastases, but
require future assessment in a multi-institutional clinical setting.

本文研究了人工智能系统在手术期间识别腹膜表面转移瘤方面的作用，发现在仿真环境下，深度学习手术辅助系统能够比直接手术更加准确的识别腹膜表面转移瘤，可以在一定程度上降低误识别率并提高手术成功率，但需要在多机构临床环境下进一步研究。