OBJECTIVE: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requirin...
OBJECTIVES: No method is available to determine the non-perfused volume (NPV) repeatedly during magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU) ablations of uterine fibroids, as repeated acquisition of contrast-enhanced T1-weigh...
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 uterin...
OBJECTIVE: To demonstrate the intraoperative use of three-dimensional (3D) imaging reconstruction for a complex case of multiple myomectomy assigned to robot-assisted laparoscopic surgery.
OBJECTIVE: This study aimed to report the first surgery for gynecological diseases using a new robotic platform, the hinotori™, and validate its feasibility in clinical settings.
PURPOSE: We present a large real-world multicentric dataset of ovarian, uterine and cervical oligometastatic lesions treated with SBRT exploring efficacy and clinical outcomes. In addition, an exploratory machine learning analysis was performed.
BACKGROUND/AIM: Vasopressin injected during myomectomy is known to effectively reduce bleeding but is sometimes associated with intraoperative vasoconstriction and hypertension due to systemic absorption. Although there is a growing preference for th...
Acta radiologica (Stockholm, Sweden : 1987)
38343091
BACKGROUND: The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition.