Conservative treatment versus surgical excision of ovarian dermoid cysts: Impact on ovarian stimulation and IVF cycle success

Co-Existence of Endometriosis with Ovarian Dermoid Cysts: A Retrospective Cohort Study

Both endometriosis and ovarian dermoid cysts are benign conditions characterized by the presence of well-differentiated tissues in ectopic locations. The presence and surgical excision of these entities can potentially impact ovarian reserves, contributing to reduced chances of future pregnancy. The objective of our study is to investigate the bidirectional association between endometriosis and ovarian dermoid cysts, as well as to analyze the clinical characteristics of patients diagnosed with both conditions. A retrospective cohort study was conducted, including women who underwent laparoscopy and received histological diagnoses of endometriosis and/or dermoid cysts between 2011 and 2019 at the Cantonal Hospital of Schaffhausen. We identified 985 women with endometriosis and 83 women with ovarian dermoid cysts. Among these groups, 22 women presented with both endometriosis and ovarian dermoid cysts. The majority of the above patients had endometriosis stage rASRM I-II (72.7%), with peritoneal endometriosis being the most common phenotype of endometriosis (77.2%). Out of the 14 patients with a desire for future pregnancy, the majority (11/14, 78.5%) had an EFI score of 7-8. The prevalence of bilateral ovarian dermoid cysts was higher in women with both ovarian dermoid cysts and endometriosis in comparison to women with ovarian dermoid cysts without endometriosis (18% vs. 6.5%). Our study revealed that 26.5% of women with ovarian dermoid cysts also had endometriosis, a notably higher prevalence than observed in the general population. Clinicians should be aware of this co-existence, and preoperative counseling should be an integral part of the care plan for affected individuals, where the potential risks and the available options for fertility preservation should be discussed in detail.

Perioperative Nursing Management of Patients Undergoing Laparoscopic Ovarian Cystectomy Guided by Ultrasound Imaging under Intelligent Algorithm

This study was aimed at exploring the application value of ultrasonic imaging-guided laparoscopic ovarian cystectomy after denoising by intelligent algorithms in perioperative nursing intervention of patients. In this study, convolutional downsampling was introduced to the UNet model, based on which the residual structure and Recon module were added to improve the UNet denoising model, which was applied to 100 patients who underwent ultrasound imaging-guided laparoscopic ovarian cystectomy. The patients were grouped into a control group receiving conventional nursing and an experimental group receiving perioperative nursing management. The various experimental indicators were comprehensively evaluated. The results revealed that after denoising using the improved UNet model, the ultrasound image showed no unnecessary interference noise, and the image clarity was significantly improved. In the experimental group, the operation time was 55.45 ± 6.13 days, the intraoperative blood loss was 71.52 ± 9.87 days, the postoperative exhaust time was 1.9 ± 0.73 days, the time to get out of bed was 1.2 ± 0.85 days, the complication rate was 8%, the hospitalization time was 7.3 ± 2.6 days, and the nursing satisfaction rate reached 98%. All above aspects were significantly better than those of the control group, and the differences were statistically significant (P < 0.05). In short, the improved UNet denoising model can effectively eliminate the interference noise in ultrasound and restore high-quality ultrasound images. Perioperative nursing intervention can accelerate the recovery speed of patients, reduce the complication rate, and shorten the length of stay in hospital. Therefore, it was worthy of being widely used in clinical nursing.