ArticleCancers2024
Detection and Classification of Hysteroscopic Images Using Deep Learning.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- 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
- See more, treat early, conceive sooner: Outpatient hysteroscopy in modern fertility care.The Journal of international medical research · 2025Article
- Article
- Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer.Scientific reports · 2024Article
- Malignancy risk factors based on endometrial polyp.BMC women's health · 2024Article
- Quantitative Assessment of Polarization and Elastic Properties of Endometrial Tissue for Precancer/Cancer Diagnostics Using Multimodal Optical Coherence Tomography.Diagnostics (Basel, Switzerland) · 2024Article
- Biotechnological progresses in modelling the human endometrium: the evolution of currentFrontiers in bioengineering and biotechnology · 2024Review
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14 authors.
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Abstract
backgroundAlthough hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings are available, with the absence of studies evaluating the performance of DL models in identifying intrauterine lesions and the possible aid related to the inclusion of clinical factors in the model.
aimTo develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images.
methodsA monocentric observational retrospective cohort study was performed by reviewing clinical records, electronic databases, and stored videos of hysteroscopies from consecutive patients with pathologically confirmed intrauterine lesions at our Center from January 2021 to May 2021. Retrieved hysteroscopic images were used to build a DL model for the classification and identification of intracavitary uterine lesions with or without the aid of clinical factors. Study outcomes were DL model diagnostic metrics in the classification and identification of intracavitary uterine lesions with and without the aid of clinical factors.
resultsWe reviewed 1500 images from 266 patients: 186 patients had benign focal lesions, 25 benign diffuse lesions, and 55 preneoplastic/neoplastic lesions. For both the classification and identification tasks, the best performance was achieved with the aid of clinical factors, with an overall precision of 80.11%, recall of 80.11%, specificity of 90.06%, F1 score of 80.11%, and accuracy of 86.74 for the classification task, and overall detection of 85.82%, precision of 93.12%, recall of 91.63%, and an F1 score of 92.37% for the identification task.
conclusionOur DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight.
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