Trial reportPloS one2019
Real-time gastric polyp detection using convolutional neural networks.
Trial report in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 3 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed, 3 syntheses or guidelines pooled it, 160 citations in OpenAlex.
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study.Journal of medical Internet research · 2022Pooled it
- Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis.Frontiers in medicine · 2022Pooled it
- Y-Polyp: research on devices for detecting colorectal polyps with limited samples.BMC medical imaging · 2026Article
- GAD-YOLO: a gastrointestinal abnormality detection YOLO model with multi-scale channel attention and residual fusion.Medical & biological engineering & computing · 2026Article
- LightAttn-YOLO-V8: an efficient colorectal polyp detection with lightweight attention mechanism based on YOLO.Scientific reports · 2026Article
- Gastrointestinal polyp detection method based on the improved RT-DETR.Scientific reports · 2026Article
- A hybrid ViT-L/32-MaxViT-L architecture with adaptive gated fusion for multiclass gastrointestinal disease detection and multi-methodFrontiers in endocrinology · 2026Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Automated endoscopic image quality assessment for robust colorectal lesion detection.BMC medical imaging · 2025Article
- A novel approach to overcome black box of AI for optical diagnosis in colonoscopy.Scientific reports · 2025Article
- Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing.Scientific data · 2025Article
- A review of deep learning methods for gastrointestinal diseases classification applied in computer-aided diagnosis system.Medical & biological engineering & computing · 2025Review
- Early detection of esophageal cancer: Evaluating AI algorithms with multi-institutional narrowband and white-light imaging data.PloS one · 2025Article
- Article
- DeepNeXt: a lightweight polyp segmentation algorithm based on multi-scale attention.Quantitative imaging in medicine and surgery · 2024Article
- CRH-YOLO for precise and efficient detection of gastrointestinal polyps.Scientific reports · 2024Article
- Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset.Health information science and systems · 2024Article
- Review
- Deep Learning Models for Anatomical Location Classification in Esophagogastroduodenoscopy Images and Videos: A Quantitative Evaluation with Clinical Data.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided polyp detection in gastric gastroscopy has been the subject of research over the past few decades. However, despite significant advances, automatic polyp detection in real time is still an unsolved problem. In this paper, we report on a convolutional neural network (CNN) for polyp detection that is constructed based on Single Shot MultiBox Detector (SSD) architecture and which we call SSD for Gastric Polyps (SSD-GPNet). To take full advantages of feature maps' information from the feature pyramid and to acquire higher accuracy, we re-use information that is abandoned by Max-Pooling layers. In other words, we reuse the lost data from the pooling layers and concatenate that data as extra feature maps to contribute to classification and detection. Meanwhile, in the feature pyramid, we concatenate feature maps of the lower layers and feature maps that are deconvolved from upper layers to make explicit relationships between layers and to effectively increase the number of channels. The results show that our enhanced SSD for gastric polyp detection can realize real-time polyp detection with 50 frames per second (FPS) and can improve the mean average precision (mAP) from 88.5% to 90.4%, with only a little loss in time-performance. And the further experiment shows that SSD-GPNet has excellent performance in improving polyp detection recalls over 10% (p = 0.00053), especially in small polyp detection. This can help endoscopic physicians more easily find missed polyps and decrease the gastric polyp miss rate. It may be applicable in daily clinical practice to reduce the burden on physicians.
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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.