ArticleCancers2022
A Deep Learning Model System for Diagnosis and Management of Adnexal Masses.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 4 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 4 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Artificial Intelligence Applied to Ultrasound Diagnosis of Pelvic Gynecological Tumors: A Systematic Review and Meta-Analysis.Gynecologic and obstetric investigation · 2026Pooled it
- Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Pooled it
- Artificial intelligence for ovarian cancer diagnosis via ultrasound: a systematic review and quantitative assessment of model performance.Frontiers in artificial intelligence · 2025Pooled it
- Artificial intelligence based on ultrasound for initial diagnosis of malignant ovarian cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Development and validation of an artificial intelligence-based model for diagnosing benign, borderline, and malignant adnexal masses.NPJ precision oncology · 2026Article
- ADNEX-AI: automated extraction of ultrasound predictors for interpretable ovarian cancer risk stratification.NPJ precision oncology · 2025Article
- Artificial Intelligence in Ultrasound-Based Diagnoses of Gynecological Tumors: A Systematic Review.Cureus · 2025Review
- Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study.EClinicalMedicine · 2024Article
- Enhancing Ovarian Tumor Diagnosis: Performance of Convolutional Neural Networks in Classifying Ovarian Masses Using Ultrasound Images.Journal of clinical medicine · 2024Article
- Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound.NPJ precision oncology · 2024Article
- Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS.Frontiers in oncology · 2024Article
- Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.Frontiers in medicine · 2024Article
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
Appropriate clinical management of adnexal masses requires a detailed diagnosis. We retrospectively collected ultrasound images of 1559 cases from the first Center of Chinese PLA General Hospital and developed a fully automatic deep learning (DL) model system to diagnose adnexal masses. The DL system contained five models: a detector, a mass segmentor, a papillary segmentor, a type classifier, and a pathological subtype classifier. To test the DL system, 462 cases from another two hospitals were recruited. The DL system identified benign, borderline, and malignant tumors with macro-F1 scores that varied from 0.684 to 0.791, a benefit to preventing both delayed and overextensive treatment. The macro-F1 scores of the pathological subtype classifier to categorize the benign masses varied from 0.714 to 0.831. The detailed classification can inform clinicians of the corresponding complications of each pathological subtype of benign tumors. The distinguishment between borderline and malignant tumors and inflammation from other subtypes of benign tumors need further study. The accuracy and sensitivity of the DL system were comparable to that of the expert and intermediate sonographers and exceeded that of the junior sonographer.
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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.