ArticleDiagnostics (Basel, Switzerland)2023
Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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
8 citing papers in PubMed.
- Artificial Intelligence, Deep Learning, and Computer Vision in Hysteroscopy: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Deep learning-based automated classification of endometrial lesions in IVF patients using hysteroscopic images.Scientific reports · 2026Article
- Endometrial polyps recurrence: risk factors, prevention, and management.Open medicine (Warsaw, Poland) · 2026Review
- See more, treat early, conceive sooner: Outpatient hysteroscopy in modern fertility care.The Journal of international medical research · 2025Article
- Review
- Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models.Medicina (Kaunas, Lithuania) · 2024Review
- Article
- Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Endometrial polyps are common gynecological lesions. The standard treatment for this condition is hysteroscopic polypectomy. However, this procedure may be accompanied by misdetection of endometrial polyps. To improve the diagnostic accuracy and reduce the risk of misdetection, a deep learning model based on YOLOX is proposed to detect endometrial polyps in real time. Group normalization is employed to improve its performance with large hysteroscopic images. In addition, we propose a video adjacent-frame association algorithm to address the problem of unstable polyp detection. Our proposed model was trained on a dataset of 11,839 images from 323 cases provided by a hospital and was tested on two datasets of 431 cases from two hospitals. The results show that the lesion-based sensitivity of the model reached 100% and 92.0% for the two test sets, compared with 95.83% and 77.33%, respectively, for the original YOLOX model. This demonstrates that the improved model may be used effectively as a diagnostic tool during clinical hysteroscopic procedures to reduce the risk of missing endometrial polyps.
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Registered trials
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