ArticleFrontiers in oncology2021
A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video).
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.Journal of medical Internet research · 2023Pooled it
- Research advances in early screening for colorectal cancer (Review).Molecular and clinical oncology · 2026Review
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Integration of clinical, pathological, and endoscopic data improves prediction for lymph node metastasis in early gastric cancer: a retrospective multicenter study.International journal of surgery (London, England) · 2026Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- A prospective and comparative study on improving the diagnostic accuracy of early gastric cancer based on deep convolutional neural network real-time diagnosis system (with video).Surgical endoscopy · 2025Article
- Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.BMC medical research methodology · 2025Article
- Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.Diagnostics (Basel, Switzerland) · 2023Review
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- Review
- RETRACTED: Convolutional neural network model by deep learning and teaching robot in keyboard musical instrument teaching.PloS one · 2023Review
- Development and validation of novel immune-inflammation-based clinical predictive nomograms in HER2-negative advanced gastric cancer.Frontiers in oncology · 2023Article
- Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy.Diagnostics (Basel, Switzerland) · 2022Review
- Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis.Sensors (Basel, Switzerland) · 2022Article
- Review
- Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks.Micromachines · 2022Article
- Multi-center verification of the influence of data ratio of training sets on test results of an AI system for detecting early gastric cancer based on the YOLO-v4 algorithm.Frontiers in oncology · 2022Article
- Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas.Cancers · 2021Review
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsPrediction of intramucosal gastric cancer (GC) is a big challenge. It is not clear whether artificial intelligence could assist endoscopists in the diagnosis.
methodsA deep convolutional neural networks (DCNN) model was developed
resultsThe DCNN model discriminated intramucosal GC from advanced GC with an AUC of 0.942 (95% CI, 0.915-0.970), a sensitivity of 90.5% (95% CI, 84.1%-95.4%), and a specificity of 85.3% (95% CI, 77.1%-90.9%) in the testing dataset. The diagnostic performance of novice endoscopists was comparable to those of expert endoscopists with the DCNN model's assistance (accuracy: 84.6%
conclusionsAn AI-assisted system was established and found useful for novice endoscopists to achieve comparable diagnostic performance with experts.
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