ReviewWorld journal of gastroenterology2020
Artificial intelligence technique in detection of early esophageal cancer.
Review in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology AssociationPooled it
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma.World journal of gastrointestinal oncology · 2026Review
- Diagnostic value of real-time computer-aided detection for precancerous lesion during esophagogastroduodenoscopy: A meta-analysis.World journal of gastrointestinal surgery · 2025Article
- Unraveling the characteristics of early esophageal neuroendocrine carcinoma using multi-model endoscopy: A retrospective study of serial cases.World journal of gastrointestinal oncology · 2025Article
- Article
- Esophageal cancer screening, early detection and treatment: Current insights and future directions.World journal of gastrointestinal oncology · 2024Review
- Assessment of hyperspectral imaging and CycleGAN-simulated narrowband techniques to detect early esophageal cancer.Scientific reports · 2023Article
- Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis.World journal of gastrointestinal oncology · 2023Article
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- Biomarkers for Early Detection, Prognosis, and Therapeutics of Esophageal Cancers.International journal of molecular sciences · 2023Review
- Intelligent Identification of Early Esophageal Cancer by Band-Selective Hyperspectral Imaging.Cancers · 2022Article
- Artificial intelligence assists precision medicine in cancer treatment.Frontiers in oncology · 2022Review
- Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning.Journal of medical systems · 2021Article
- Modern Diagnosis of Early Esophageal Cancer: From Blood Biomarkers to Advanced Endoscopy and Artificial Intelligence.Cancers · 2021Review
- New Trends in Esophageal Cancer Management.Cancers · 2021Article
- Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review.World journal of gastroenterology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the rapid progression and poor prognosis of esophageal cancer (EC), the early detection and diagnosis of early EC are of great value for the prognosis improvement of patients. However, the endoscopic detection of early EC, especially Barrett's dysplasia or squamous epithelial dysplasia, is difficult. Therefore, the requirement for more efficient methods of detection and characterization of early EC has led to intensive research in the field of artificial intelligence (AI). Deep learning (DL) has brought about breakthroughs in processing images, videos, and other aspects, whereas convolutional neural networks (CNNs) have shone lights on detection of endoscopic images and videos. Many studies on CNNs in endoscopic analysis of early EC demonstrate excellent performance including sensitivity and specificity and progress gradually from
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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.