ArticleAnnals of medicine2025
Virtual NBI image synthesis using stable diffusion for enhanced recognition of early gastric cancer: a technical validation study.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- AI Revolution in Upper GI: Endoscopic Diagnosis of Gastric Cancers and Premalignant Neoplasms.Diseases (Basel, Switzerland) · 2026Review
- Application of Artificial Intelligence in the Endoscopic Diagnosis of Gastric Cancer and Precancerous Lesions.Diagnostics (Basel, Switzerland) · 2026Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNarrow band imaging (NBI) can assist endoscopists in detecting early gastric cancer (EGC) more easily, but its widespread use is hindered by economic cost and technical property rights. We aim to realize the conversion of white light endoscopy (WLE) images into virtual narrow band imaging (Vir-NBI) images using stable diffusion.
methodsEndoscopic images were retrospectively collected from 325 patients who underwent endoscopic submucosal dissection (ESD). A total of 273 NBI images from 218 patients were used to fine-tune stable diffusion, which then converted 111 WLE images from 107 patients into Vir-NBI images. Endoscopists assessed the images and evaluated their effectiveness in diagnosing EGC and depicting lesion margins in the form of WLE, NBI, and Vir-NBI image pairs.
resultsCompared with WLE images, Vir-NBI images have better quality. The accuracy of junior endoscopists in diagnosing EGC by observing WLE images alone, simultaneous WLE and NBI images, and simultaneous WLE and Vir-NBI images were 61.26%, 79.28% and 81.08%, respectively. For intermediate endoscopists, the diagnostic accuracy was 72.07%, 86.79% and 84.68%, respectively. For senior endoscopists, the diagnostic accuracy was 80.18%, 95.50% and 92.79%, respectively. In addition,Vir-NBI images had higher area concordance rate andsuccessful whole-lesion diagnosis than WLE images (43.85% vs 39.32%,
conclusionsVir-NBI images has similar observation effect with real NBI image, which helps endoscopists better visualize the lesion structure, thus improving the accuracy of EGC diagnosis.
Indexed as
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.