ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2022
Development and evaluation of a double-check support system using artificial intelligence in endoscopic screening for gastric cancer.
Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 23 citations in OpenAlex.
- Evaluation of Computer-aided Detection for Identifying Missed Gastric Cancer After Endoscopic Submucosal Dissection.DEN open · 2027Article
- Effect of Helicobacter pylori infection on deep learning-assisted detection of gastric neoplastic lesions under white light endoscopy.Surgical endoscopy · 2026Article
- Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects.Annals of medicine · 2025Review
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.Cancer letters · 2025Review
- Enhancing cancer risk awareness and screening management through artificial intelligence: a narrative review.Frontiers in oncology · 2025Review
- Biological Clock Genes are Crucial and Promising Biomarkers for the Therapeutic Targets and Prognostic Assessment in Gastric Cancer.Journal of gastrointestinal cancer · 2024Article
- Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images.Scientific reports · 2024Article
- Clinical and endoscopic features of metachronous gastric cancer with possible lymph node metastasis after endoscopic submucosal dissection and Helicobacter pylori eradication.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2023Article
- A Global Perspective on Gastric Cancer Screening: Which Concepts Are Feasible, and When?Cancers · 2023Review
- Current status and future perspective of linked color imaging for gastric cancer screening: a literature review.Journal of gastroenterology · 2023Review
- Gastric Cancer Screening in Japan: A Narrative Review.Journal of clinical medicine · 2022Review
Corrections and comments
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Authors and funding
20 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThis study aimed to prevent missing gastric cancer and point out low-quality images by developing a double-check support system (DCSS) for esophagogastroduodenoscopy (EGD) still images using artificial intelligence.
methodsWe extracted 12,977 still EGD images from 855 cases with cancer [821 with early gastric carcinoma (EGC) and 34 malignant lymphoma (ML)] and developed a lesion detection system using 10,994 images. The remaining images were used as a test dataset. Additional validation was performed using a new dataset containing 50 EGC and 1,200 non-GC images by comparing the interpretation of ten endoscopists (five trainees and five experts). Furthermore, we developed another system to detect low-quality images, which are not suitable for diagnosis, using 2198 images.
resultsIn the validation of 1983 images from the 124 cancer cases, the DCSS diagnosed cancer with a sensitivity of 89.2%, positive predictive value (PPV) of 93.3%, and an accuracy of 83.3%. EGC was detected in 93.2% and ML in 92.5% of cases. Comparing with the endoscopists, sensitivity was significantly higher in the DCSS, and the average diagnostic time was significantly shorter using the DCSS than that by the trainees. The sensitivity, specificity, PPV, and accuracy in detecting low-quality images were 65.8%, 93.1%, 79.6%, and 85.2% for "Blur" and 57.8%, 91.7%, 82.2%, and 78.1% for "Mucus adhesion," respectively.
conclusionsThe DCSS showed excellent capability in detecting lesions and pointing out low-quality images.
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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.