The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Real-Time Automatic Assisted Detection of Uterine Fibroid in Ultrasound Images Using a Deep Learning Detector
Abstract
Objective Uterine smooth muscle hyperplasia causes a tumor called a uterine fibroid. With an incidence of up to 30%, it is one of the most prevalent tumors in women and has the third highest prevalence of all gynecological illnesses. Although uterine fibroids are usually not accompanied by symptoms, there are physical effects, such as impairment of the ability to conceive. To reduce morbidity, early detection and treatment are crucial. Ultrasound imaging is a common method used for pre-operative guidance and interventional therapy. Many applications of object detection are performing well with the advancement of deep learning in the field of medical image analysis. To ensure accuracy, computer-assisted detection can further solve the subjective problem generated by different doctors when they read images.Methods Our research provides an improved YOLOv3 that combines the properties of EfficientNet and YOLOv3, which use a convolutional neural network to extract features, to detect uterine fibroid ultrasound images.Results Our approach attained an F1 score of 95% and an average precision of 98.38% and reached a detection speed of 0.28 s per image. We reviewed and analyzed several detection techniques and identified potential future research hotpots.Conclusion This technique offers enough supplementary diagnostic tools for amateur or expert ultrasonologists and sets a solid foundation for future medical care and surgical excision.
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“Yang et al. proposed an AI algorithm for the detection of fibroids, which facilitated pre-operative guidance and interventional therapy [221].”
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Sorted by citations
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44
“In total, 11 articles were summarized in this section, including the topics of endometriosis [216,217], premature ovarian failure [218,219], uterine fibroids [220,221], follicle tracking [222,223], and ectopic pregnancies [224,225].”
Background
Metadata
- Authors
- Yang, T., Yuan, L., Li, P., & Liu, P.
- Affiliations
- Huaqiao University (CN)
- Corresponding author
- Liu, Peizhong
- Venue
- Ultrasound in Medicine & Biology
- Publisher
- Elsevier BV
- Published
- 2023-07
- Volume
- 49
- Issue
- 7
- Pages
- 1616-1626
- ISSN
- 0301-5629 (ISSN-L)
- OpenAlex
- W4367305606
- Cited by
- 47
- References
- 35
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 10 · 2023: 6
- FWCI
- 1.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ultrasound in Medicine & Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Obstetrics and Gynecology › Uterine Myomas and Treatments
- Keywords
- uterine fibroids, uterine smooth muscle tumors, computer-aided detection, ultrasound image analysis, YOLOv3, EfficientNet, object detection, deep learning, early cancer detection
- MeSH terms
- Deep Learning, Female, Humans, Leiomyoma, Ultrasonography, Uterine Neoplasms, Uterus, Neural Networks, Computer
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