AIMC Topic: Uterine Neoplasms
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A superpixel based self-attention network for uterine fibroid segmentation in high intensity focused ultrasound guidance images.
Scientific reports
Jul 1, 2025
Ultrasound guidance images are widely used for high intensity focused ultrasound (HIFU) therapy; however, the speckles, acoustic shadows, and signal attenuation in ultrasound guidance images hinder the observation of the images by radiologists and ma...
Peritoneal cytology predicting distant metastasis in uterine carcinosarcoma: machine learning model development and validation.
World journal of surgical oncology
Apr 26, 2025
OBJECTIVE: This study develops and validates a machine learning model using peritoneal cytology to predict distant metastasis in uterine carcinosarcoma, aiding clinical decision-making.
FreqYOLO: A uterine disease detection network based on local and global frequency feature learning.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 4, 2025
Leiomyomas (LM) and adenomyosis (AM) are common gynecological diseases with high incidence rates and an increasing trend of affecting younger women. Accurate detection and differentiation of LM and AM in ultrasound images are crucial for selecting ap...
Machine learning models for prediction of NPVR ≥80% with HIFU ablation for uterine fibroids.
International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group
Mar 23, 2025
BACKGROUND: Currently high-intensity focused ultrasound (HIFU) is widely used to treat uterine fibroids (UFs). The aim of this study is to develop a machine learning model that can accurately predict the efficacy of HIFU ablation for UFs, assisting t...
Deep learning based uterine fibroid detection in ultrasound images.
BMC medical imaging
Aug 19, 2024
Uterine fibroids are common benign tumors originating from the uterus's smooth muscle layer, often leading to symptoms such as pelvic pain, and reproductive issues. Early detection is crucial to prevent complications such as infertility or the need f...
Integrated Transcriptomic Landscape and Deep Learning Based Survival Prediction in Uterine Sarcomas.
Cancer research and treatment
Jul 10, 2024
PURPOSE: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs).
Predicting the Prognosis of HIFU Ablation of Uterine Fibroids Using a Deep Learning-Based 3D Super-Resolution DWI Radiomics Model: A Multicenter Study.
Academic radiology
Jul 4, 2024
RATIONALE AND OBJECTIVES: To assess the feasibility and efficacy of a deep learning-based three-dimensional (3D) super-resolution diffusion-weighted imaging (DWI) radiomics model in predicting the prognosis of high-intensity focused ultrasound (HIFU)...
Development and validation of a deep learning-based method for automatic measurement of uterus, fibroid, and ablated volume in MRI after MR-HIFU treatment of uterine fibroids.
European journal of radiology
Jul 3, 2024
INTRODUCTION: The non-perfused volume divided by total fibroid load (NPV/TFL) is a predictive outcome parameter for MRI-guided high-intensity focused ultrasound (MR-HIFU) treatments of uterine fibroids, which is related to long-term symptom relief. I...
Empowering gynaecologists with Artificial Intelligence: Tailoring surgical solutions for fibroids.
European journal of obstetrics, gynecology, and reproductive biology
Jun 3, 2024
BACKGROUND: In recent years, the integration ofArtificial intelligence (AI) into various fields of medicine including Gynaecology, has shown promising potential. Surgical treatment of fibroid is myomectomy if uterine preservation and fertility are th...
A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning.
Journal of clinical ultrasound : JCU
Apr 27, 2024
PURPOSE: Uterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accuracy of prognosis may also be affected by way of doctor inexperience and fatigue, underscoring the wa...
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