ArticleNPJ digital medicine2025
Artificial intelligence assisted real-time recognition of intra-abdominal metastasis during laparoscopic gastric cancer surgery.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of artificial intelligence in peripheral neurosurgery: a systematic review.Neurosurgical review · 2026Pooled it
- Merging artificial intelligence into cancer nursing care: Current applications, challenges, and opportunities.Asia-Pacific journal of oncology nursing · 2026Article
- AI-Based Automated Instrument Tracking During Endoscopic Skull Base Surgery: A Pilot Feasibility Study.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Artificial intelligence-driven real-time assistance in minimally invasive surgery: a technology-oriented narrative review.Journal of minimally invasive surgery · 2026Review
- Next-Generation Surgery: Integrating Artificial Intelligence, Genetic Technologies, Bioengineering and Rehabilitation Into Modern Practices.Exploration (Beijing, China) · 2026Article
- Review
- Multimodal machine learning for staging laparoscopy: a combined image analysis and morphologic tool for the discrimination of peritoneal metastasis.International journal of surgery (London, England) · 2026Article
- Foundation models: Insights and implications for gastrointestinal cancer.World journal of gastroenterology · 2025Review
- AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.Journal of nanobiotechnology · 2025Review
- Exploiting artificial intelligence in precision oncology: an updated comprehensive review.Journal of translational medicine · 2025Review
- Clinical validation of an AI-assisted system for real-time kidney stone detection during flexible ureteroscopic surgery.NPJ digital medicine · 2025Article
- Treatment of giant choledochal cysts with combined surgery and percutaneous transhepatic cholangial drainage: A case report.World journal of gastrointestinal surgery · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.