No abstract

Objectives: A growing number of studies suggest that the incomplete healing of the CS scar in the uterus increase the risk of uterine dehiscence or rupture during subsequent pregnancies. Thus, the factors that affect wound healing should be evaluated. We aimed to determine whether the morphology of the CS scar in non-pregnant women after one elective CS was affected by the site of the uterine incision, uterine flexion, maternal age, and fetal birth weight. Material and methods:208 non-pregnant women were invited for participation in the study, but only 101 of them met inclusion criteria. Standardized scar parameters (residual myometrial thickness (RMT), depth (D) and width (W) of the hypoechoic niche) were measured using ultrasonography at least 6 weeks after the CS.Results: Scar defect was detected in 26 of 101 subjects. Women without scar defect had significantly higher RMT values (1.87 vs. 0.87), lower newborn birth weight (3127 g vs. 3295 g), and higher scar location above the internal cervical os (62% vs. 16%), than those with scar defect. Maternal age was significantly correlated with D value (R = 0.40). Uterine retroflexion was significantly correlated with a larger D value (R = 0.63) and a larger D/RMT ratio (R = 0.24). Conclusions:In low-risk women who have undergone one elective CS, several risk factors are associated with development of the scar defect, but only scar location can be modified during surgery. Future research is needed to determine whether a relatively higher incision location in the uterus can ensure optimal healing of the CS scar.

A surgical repair procedure for an incompletely healed uterine cesarean scar is effective in increasing RMT thickness, decreasing the depth of the scar, and reducing symptoms related to the cesarean section scar defect. Further studies on post-repair pregnancy outcomes are required to evaluate whether the procedure affects the rate of cesarean scar pregnancy, morbidly adherent placenta, and/or uterine scar dehiscence and rupture. The repair of a cesarean scar defect is recommended only for symptomatic women. © 2017 Wiley Periodicals, Inc. J Clin Ultrasound 45:455-460, 2017.

This article has been peer reviewed and published immediately upon acceptance.It is an open access article, which means that it can be downloaded, printed, and distributed freely, provided the work is properly cited. Articles in "Ginekologia Polska" are listed in PubMed.

The rising global incidence of cervical cancer is estimated to have affected more than 600,000 women, and nearly 350,000 women are predicted to have died from the disease in 2020 alone. Novel advances in cancer prevention, screening, diagnosis and treatment have all but reduced the burden of cervical cancer in developed nations. Unfortunately, cervical cancer is still the number one gynecological cancer globally. A limiting factor in managing cervical cancer globally is access to healthcare systems and trained medical personnel. Any methodology or procedure that may simplify or assist cervical cancer screening is desirable. Herein, we assess the use of artificial intelligence (AI)-assisted colposcopy in a tertiary hospital cervical diagnostic pathology unit. The study group consisted of 48 women (mean age 34) who were referred to the clinic for a routine colposcopy by their gynecologist. Cervical images were taken by an EVA-Visualcheck TM colposcope and run through an AI algorithm that gave real-time binary results of the cervical images as being either normal or abnormal. The primary endpoint of the study assessed the AI algorithm’s ability to correctly identify histopathology results of CIN2+ as being abnormal. A secondary endpoint was a comparison between the AI algorithm and the clinical assessment results. Overall, we saw lower sensitivity of AI (66.7%; 12/18) compared with the clinical assessment (100%; 18/18), and histopathology results as the gold standard. The positive predictive value (PPV) was comparable between AI (42.9%; 12/28) and the clinical assessment (41.8%; 18/43). The specificity, however, was higher in the AI algorithm (46.7%; 14/30) compared to the clinical assessment (16.7%; 5/30). Comparing the congruence between the AI algorithm and histopathology results showed agreement 54.2% of the time and disagreement 45.8% of the time. A trained colposcopist was in agreement 47.9% and disagreement 52.1% of the time. Assessing these results, there is currently no added benefit of using the AI algorithm as a tool of speeding up diagnosis. However, given the steady improvements in the AI field, we believe that AI-assisted colposcopy may be of use in the future.