Artificial intelligence assisted real-time recognition of intra-abdominal metastasis during laparoscopic gastric cancer surgery - Hao Chen; Longfei Gou; Zhiwen Fang; Qi Dou; Haobin Chen; Chang Chen; Yuqing Qiu; Jinglin Zhang; Chenglin Ning; Yanfeng Hu; Haijun Deng; Jiang Yu; et al. (npj Digital Medicine)
Abstract Laparoscopic exploration (LE) is crucial for diagnosing intra-abdominal metastasis (IAM) in advanced gastric cancer (GC). However, overlooking single, tiny, and occult IAM lesions during LE can severely affect the treatment and prognosis due to surgeons’ visual misinterpretations. To address this, we developed the artificial intelligence laparoscopic exploration system (AiLES) to recognize IAM lesions with various metastatic extents and locations. The AiLES was developed based on a dataset consisting of 5111 frames from 100 videos, using 4130 frames for model development and 981 frames for evaluation. The AiLES achieved a Dice score of 0.76 and a recognition speed of 11 frames per second, demonstrating robust performance in different metastatic extents (0.74–0.76) and locations (0.63–0.90). Furthermore, AiLES performed comparably to novice surgeons in IAM recognition and excelled in recognizing tiny and occult lesions. Our results demonstrate that the implementation of AiLES could enhance accurate tumor staging and assist individualized treatment decisions.