SynthesisBMC medical informatics and decision making2023
Machine learning applications for early detection of esophageal cancer: a systematic review.
Synthesis in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
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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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Emerging applications of artificial intelligence for risk stratification in head and neck cancer: a scoping review.Frontiers in oncology · 2026Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Accuracy of machine learning in detecting lymph node metastasis of esophageal cancer: a systematic review and meta-analysis.Translational cancer research · 2026Article
- Article
- Building an interpretable machine learning prognosis prediction model-based on baseline examinations of patients with esophageal cancer undergoing surgery.Frontiers in oncology · 2026Article
- Artificial intelligence in surgical decision-making across the perioperative continuum: a scoping review.Frontiers in digital health · 2026Review
- Detection of cystoid macular edema in patients with retinitis pigmentosa based on deep learning.International journal of retina and vitreous · 2025Article
- Early Detection of Cystoid Macular Edema in Retinitis Pigmentosa Using Longitudinal Deep Learning Analysis of OCT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Machine learning techniques in hepatic encephalopathy: a scoping review.BMC medical informatics and decision making · 2025Article
- Machine Learning-based Complementary Artificial Intelligence Model for Dermoscopic Diagnosis of Pigmented Skin Lesions in Resource-limited Settings.Plastic and reconstructive surgery. Global open · 2025Article
- Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).Oncology letters · 2025Review
- Research status and progress of deep learning in automatic esophageal cancer detection.World journal of gastrointestinal oncology · 2025Review
- A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights.BMC gastroenterology · 2025Article
- Machine learning-enabled prediction of bone metastasis in esophageal cancer.Frontiers in medicine · 2025Article
- Computer-aided diagnostic system with automated deep learning method based on the AutoGluon framework improved the diagnostic accuracy of early esophageal cancer.Journal of gastrointestinal oncology · 2024Article
- Influencing factors of knowledge proficiency of general practitioners in rural China for esophageal cancer prevention and treatment: a cross-sectional study.Primary health care research & development · 2024Article
- Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEsophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC.
methodsWe conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL (", "Neural Networks (NN)", "Esophagus", "EC" and "Early Detection". After applying inclusion and exclusion criteria, 31 articles were retained for full review.
resultsThe results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods.
conclusionOur findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.
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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.