ArticlePloS one2021
Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnosis Test Accuracy of Artificial Intelligence for Endometrial Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- 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
- Diagnostic performance of artificial intelligence for detecting peritoneal and small bowel dissemination in epithelial ovarian cancer using preoperative contrast-enhanced CT imaging.Scientific reports · 2026Article
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- Article
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- MRI-derived deep-feature model for differentiating stage I endometrial carcinoma from atypical endometrial hyperplasia: a two-center retrospective study.Frontiers in oncology · 2026Article
- Biological characteristics prediction of endometrial cancer based on deep convolutional neural network and multiparametric MRI radiomics.Abdominal radiology (New York) · 2025Article
- See more, treat early, conceive sooner: Outpatient hysteroscopy in modern fertility care.The Journal of international medical research · 2025Article
- Learning Guided Binary PSO Algorithm for Feature Selection and Reconstruction of Ultrasound Contrast Images in Endometrial Region Detection.Biomimetics (Basel, Switzerland) · 2025Article
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- Artificial Intelligence and Hysteroscopy: A Multicentric Study on Automated Classification of Pleomorphic Lesions.Cancers · 2025Article
- Review
- Review
- Research progress of artificial intelligence in the early screening, diagnosis, precise treatment and prognosis prediction of three central gynecological malignancies.Frontiers in oncology · 2025Review
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Advancements in Minimally Invasive Techniques and Biomarkers for the Early Detection of Endometrial Cancer: A Comprehensive Review of Novel Diagnostic Approaches and Clinical Implications.Journal of clinical medicine · 2024Review
Corrections and comments
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening technique to date, early diagnosis of endometrial cancer assumes critical importance. This paper presents an artificial-intelligence-based system to detect the regions affected by endometrial cancer automatically from hysteroscopic images. In this study, 177 patients (60 with normal endometrium, 21 with uterine myoma, 60 with endometrial polyp, 15 with atypical endometrial hyperplasia, and 21 with endometrial cancer) with a history of hysteroscopy were recruited. Machine-learning techniques based on three popular deep neural network models were employed, and a continuity-analysis method was developed to enhance the accuracy of cancer diagnosis. Finally, we investigated if the accuracy could be improved by combining all the trained models. The results reveal that the diagnosis accuracy was approximately 80% (78.91-80.93%) when using the standard method, and it increased to 89% (83.94-89.13%) and exceeded 90% (i.e., 90.29%) when employing the proposed continuity analysis and combining the three neural networks, respectively. The corresponding sensitivity and specificity equaled 91.66% and 89.36%, respectively. These findings demonstrate the proposed method to be sufficient to facilitate timely diagnosis of endometrial cancer in the near future.
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