ReviewWorld journal of gastrointestinal oncology2025
Research status and progress of deep learning in automatic esophageal cancer detection.
Review in World journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- A YOLO Ensemble Framework for Detection of Barrett's Esophagus Lesions in Endoscopic Images.Diagnostics (Basel, Switzerland) · 2025Article
- Detection of protein-losing enteropathy (PLE) ultrasonographic imaging features in dogs using deep learning neural networks.Frontiers in artificial intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Esophageal cancer (EC), a common malignant tumor of the digestive tract, requires early diagnosis and timely treatment to improve patient prognosis. Automated detection of EC using medical imaging has the potential to increase screening efficiency and diagnostic accuracy, thereby significantly improving long-term survival rates and the quality of life of patients. Recent advances in deep learning (DL), particularly convolutional neural networks, have demonstrated remarkable performance in medical imaging analysis. These techniques have shown significant progress in the automated identification of malignant tumors, quantitative analysis of lesions, and improvement in diagnostic accuracy and efficiency. This article comprehensively examines the research progress of DL in medical imaging for EC, covering various imaging modalities such as digital pathology, endoscopy, computed tomography,
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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.