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. Therefore, providing a case-specific likelihood that respects the characteristics of each pregnancy will refine counseling, lower the decision conflict, and improve the success rate of vaginal birth trials eventually improving maternal and fetal outcomes. This paper aims to develop a clinical decision support system to evaluate the individualized likelihood mode of delivery for pregnant women with a previous lower segment cesarean section based on their unique characteristics. The study included six hundred fifty-nine pregnant women, where three hundred twenty-seven records had missing values. Various pre-processing steps, including missing data imputation and feature selection, were applied to the original dataset before model development to improve the data quality. Missing values were handled first, then a feature selection process using a genetic algorithm was applied to select the relevant features and to exclude features that may have been affected negatively by missing data imputation. After that, four machine learning classifiers, namely Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression, were used to build the prediction model. The results showed that imputing missing values followed by feature selection was more efficient than deleting them since the Area Under the Curve (AUC) has increased from 0.655 to 0.812 using the KNN classifier.

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. Two sets of patients were created: one for the antenatal model and the other for the intrapartum model. The predicted probability for each woman was calculated and compared with the successful VBAC for each category. The predictive ability was assessed with a receiver operating characteristic, and the area under the curve (AUC) was determined. RESULTS: There were seven hundred and fourteen complete cases for the antenatal model and six hundred ninety-seven for the intrapartum model. Our population's overall number of VBAC is 83.89% for the antenatal group and 82.92% for the intrapartum group. The mean predicted probability for a successful intended VBAC using the antenatal and intrapartum models were 79.53 ± 13.47 and 78.64 ± 14.03, respectively. The antenatal and intrapartum predictive models ROC had an AUC of 65% (95% CI: 60%-71%) and 64% (95% CI: 58%-69%), respectively. CONCLUSIONS: Both models are validated in the Jordanian population. Adapting the antenatal model as supporting evidence can lead to a higher rate of TOLAC.

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. The model will predict whether the pregnancy will continue beyond 26 weeks' gestation and the potential value of adding the cerclage in prolonging the pregnancy. The second aim is to develop a model that predicts the timing of spontaneous delivery in this high risk cohort after cerclage. The model will help treating physicians to define the chronology of management in relation to the risk of preterm birth, reducing the neonatal complications associated with it. Data from 274 pregnancies managed with cervical cerclage were included. 29 of the procedures involved multiple pregnancies. To build the first model, a data balancing technique called SMOTE was applied to overcome the problem of highly imbalanced class distribution in the dataset. After that, four classification models, namely Decision Tree, Random Forest, K-Nearest Neighbors (K-NN), and Neural Network (NN) were used to build the prediction model. The results showed that Random Forest classifier gave the best results in terms of G-mean and sensitivity with values of 0.96 and 1.00, respectively. These results were achieved at an oversampling ratio of 200%. For the second prediction model, five classification models were used to predict the time of spontaneous delivery; linear regression, Gaussian process, Random Forest, K-star, and LWL classifier. The Random Forest classifier performed best, with 0.752 correlation value. In conclusion, computational models can be developed to predict the need for cerclage and the gestation of delivery after this procedure. These models have moderate/high sensitivity for clinical application.

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. We also investigate the application of the topological representation as a constraint in target object segmentation. Ten non-small cell lung tumor CT studies were used for segmentation accuracy evaluation. The image representation results showed that the proposed tree structure retained the nesting and spatial relations of the tissues or objects in the CT image. The segmentation results demonstrated its usability in the separation of adjacent objects with similar intensity distributions.