Vaginal birth after cesarean section prediction model for Jordanian population.

The rate of cesarean section has increased significantly worldwide, creating a group of women with one lower segment cesarean section concerned about the mode of delivery in their future pregnancies. This group of mothers will face a complex discussion because the likelihood for a successful vaginal birth after cesarean section provided to them is a general one. The probability of having a successful vaginal birth is the cornerstone factor of the mothers' decision.

Validation of the Grobman model for successful vaginal birth after cesarean section in Jordanian women.

Background: To validate both models of Grobman nomogram (The antenatal and the intrapartum model) for predicting successful intended Vaginal Birth After Caesarean delivery (VBAC) in a Jordanian population.

Methods: A retrospective study has identified all live, singleton, term, cephalic pregnancies with a previous lower segment cesarean section who opted for a Trial Of Labour After Caesarean Section (TOLAC) between January 2014 to December 2020. Five variables were used for the antenatal model, while ten variables were used for the intrapartum model.

Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage.

Preterm birth, defined as a delivery before 37 weeks' gestation, continues to affect 8-15% of all pregnancies and is associated with significant neonatal morbidity and mortality. Effective prediction of timing of delivery among women identified to be at significant risk for preterm birth would allow proper implementation of prophylactic therapeutic interventions. This paper aims first to develop a model that acts as a decision support system for pregnant women at high risk of delivering prematurely before having cervical cerclage.

Structure and location preserving topological representation with applications on CT segmentation.

Contour tree represents the nesting relations of the individual components in the image; however, it neglects the geometric structure of the terrain. In this paper, we propose a new topological representation that provides the nesting and spatial relations of regions for CT image interpretation. The tree is constructed based on the signed distance transformation of binary CT image, and combines intensity based contour tree and a new gradient based topology tree.