ArticleJournal of translational medicine2021
Deep learning model for classifying endometrial lesions.
Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 89 citations in OpenAlex.
- Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.Journal of medical Internet research · 2023Pooled it
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- 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
- Article
- A metabolic marker-based diagnostic model for precancerous and malignant endometrial lesions in insulin-resistant PCOS women with sonographically suspected endometrial polyps.Frontiers in oncology · 2026Article
- Artificial intelligence in endometrial cancer: multimodal deep learning and future perspectives in precision gynecologic oncology - a structured narrative review.Frontiers in oncology · 2026Review
- MRI-derived deep-feature model for differentiating stage I endometrial carcinoma from atypical endometrial hyperplasia: a two-center retrospective study.Frontiers in oncology · 2026Article
- Histopathology Images-Based Deep Learning Prediction of Histological Types in Endometrial Cancer.Cancer medicine · 2026Article
- Impact of hysteroscopy on pregnancy outcomes in women with endometriosis-associated infertility undergoing their first frozen-thawed embryo transfer: a retrospective cohort study.Frontiers in medicine · 2026Article
- Article
- Artificial Intelligence and Hysteroscopy: A Multicentric Study on Automated Classification of Pleomorphic Lesions.Cancers · 2025Article
- Anthropometric Measurements for Predicting Low Appendicular Lean Mass Index for the Diagnosis of Sarcopenia: A Machine Learning Model.Journal of functional morphology and kinesiology · 2025Article
- Review
- Hysteroscopy: where did we start, and where are we now? The compelling story of what many considered the "Cinderella" of gynecological endoscopy.Archives of gynecology and obstetrics · 2024Review
- Quantitative Assessment of Polarization and Elastic Properties of Endometrial Tissue for Precancer/Cancer Diagnostics Using Multimodal Optical Coherence Tomography.Diagnostics (Basel, Switzerland) · 2024Article
- Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models.Medicina (Kaunas, Lithuania) · 2024Review
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHysteroscopy is a commonly used technique for diagnosing endometrial lesions. It is essential to develop an objective model to aid clinicians in lesion diagnosis, as each type of lesion has a distinct treatment, and judgments of hysteroscopists are relatively subjective. This study constructs a convolutional neural network model that can automatically classify endometrial lesions using hysteroscopic images as input.
methodsAll histopathologically confirmed endometrial lesion images were obtained from the Shengjing Hospital of China Medical University, including endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyps, and submucous myomas. The study included 1851 images from 454 patients. After the images were preprocessed (histogram equalization, addition of noise, rotations, and flips), a training set of 6478 images was input into a tuned VGGNet-16 model; 250 images were used as the test set to evaluate the model's performance. Thereafter, we compared the model's results with the diagnosis of gynecologists.
resultsThe overall accuracy of the VGGNet-16 model in classifying endometrial lesions is 80.8%. Its sensitivity to endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyp, and submucous myoma is 84.0%, 68.0%, 78.0%, 94.0%, and 80.0%, respectively; for these diagnoses, the model's specificity is 92.5%, 95.5%, 96.5%, 95.0%, and 96.5%, respectively. When classifying lesions as benign or as premalignant/malignant, the VGGNet-16 model's accuracy, sensitivity, and specificity are 90.8%, 83.0%, and 96.0%, respectively. The diagnostic performance of the VGGNet-16 model is slightly better than that of the three gynecologists in both classification tasks. With the aid of the model, the overall accuracy of the diagnosis of endometrial lesions by gynecologists can be improved.
conclusionsThe VGGNet-16 model performs well in classifying endometrial lesions from hysteroscopic images and can provide objective diagnostic evidence for hysteroscopists.
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