ArticleInternational journal of computer assisted radiology and surgery2019
Early esophageal adenocarcinoma detection using deep learning methods.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 5 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 5 syntheses or guidelines pooled it, 95 citations in OpenAlex.
- Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2024Pooled it
- The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2023Pooled it
- Machine learning applications for early detection of esophageal cancer: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases.Alimentary pharmacology & therapeutics · 2022Pooled it
- Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Optimizing Esophageal Cancer Diagnosis with Computer-Aided Detection by YOLO Models Combined with Hyperspectral Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Future of image enhanced endoscopy of esophageal adenocarcinoma.Clinical endoscopy · 2025Review
- Research status and progress of deep learning in automatic esophageal cancer detection.World journal of gastrointestinal oncology · 2025Review
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- DeepCSFusion: Deep Compressive Sensing Fusion for Efficient COVID-19 Classification.Journal of imaging informatics in medicine · 2024Article
- Semi-automatic standardized analysis method to objectively evaluate near-infrared fluorescent dyes in image-guided surgery.Journal of biomedical optics · 2024Article
- 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
- Deep Learning in Barrett's Esophagus Diagnosis: Current Status and Future Directions.Bioengineering (Basel, Switzerland) · 2023Review
- Towards a robust and compact deep learning system for primary detection of early Barrett's neoplasia: Initial image-based results of training on a multi-center retrospectively collected data set.United European gastroenterology journal · 2023Article
- Role of artificial intelligence in diagnosing Barrett's esophagus-related neoplasia.Clinical endoscopy · 2023Review
- Pattern Classification Using Quantized Neural Networks for FPGA-Based Low-Power IoT Devices.Sensors (Basel, Switzerland) · 2022Article
- A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal squamous cell carcinoma based on endoscopic images using deep learning.The British journal of radiology · 2022Article
- Predictive effect of J waves on cardiac compression and clinical prognosis of esophageal tumors: a retrospective study.Journal of gastrointestinal oncology · 2022Article
- Artificial Intelligence in Endoscopy.Digestive diseases and sciences · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically identify esophageal adenocarcinoma (EAC) regions from high-definition white light endoscopy (HD-WLE) images.
methodSeveral state-of-the-art object detection methods using Convolutional Neural Networks (CNNs) were adapted to automatically detect abnormal regions in the esophagus HD-WLE images, utilizing VGG'16 as the backbone architecture for feature extraction. Those methods are Regional-based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R-CNN and Single-Shot Multibox Detector (SSD). For the evaluation of the different methods, 100 images from 39 patients that have been manually annotated by five experienced clinicians as ground truth have been tested.
resultsExperimental results illustrate that the SSD and Faster R-CNN networks show promising results, and the SSD outperforms other methods achieving a sensitivity of 0.96, specificity of 0.92 and F-measure of 0.94. Additionally, the Average Recall Rate of the Faster R-CNN in locating the EAC region accurately is 0.83.
conclusionIn this paper, recent deep learning object detection methods are adapted to detect esophageal abnormalities automatically. The evaluation of the methods proved its ability to locate abnormal regions in the esophagus from endoscopic images. The automatic detection is a crucial step that may help early detection and treatment of EAC and also can improve automatic tumor segmentation to monitor its growth and treatment outcome.
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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.