Predictive Models for Estimating the Probability of Successful Vaginal Birth After Cesarean Delivery

Abstract

To systematically review all studies that developed or validated a vaginal birth after cesarean (VBAC) prediction model.

Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making

“…Our study distinguishes itself by achieving a comparable predictive performance with only seven parameters, a significant reduction compared to that in existing research. 4 , 5 Our strategy consisted of a two-fold approach: during the training phase, we employed boosting techniques and applied bootstrapping for rigorous testing. 31 , 32 Our results demonstrated that when analyzing tabular data, the tree-based models consistently outperformed the regression models.…”

“… 2 , 3 Nevertheless, the decision-making processes regarding VBAC are complex and multifaceted, necessitating a careful consideration of various medical, obstetric, and individual factors. 4 , 5 …”

“…Artificial intelligence (AI) has emerged as a powerful tool in healthcare, offering the potential to support medical professionals and patients in making informed decisions. 4 , 5 Drawing on previous research findings, 6 – 9 we created an innovative web-based platform serving as the basis for a decision-aid (DA) system for birth choices aimed at fostering shared decision-making (SDM). 10 Through this DA platform, we strive to enhance understanding, boost confidence, and improve communication between medical professionals and pregnant women.…”

“…Although several VBAC prediction models have been developed, their generalizability and applicability remain unclear 4 , 5 ; thus, high-quality internal and external validation studies are needed. Most importantly, none of these VBAC prediction models intergrade the AI technique into the DA platform.…”

The Future of Prediction Modeling in Clinical Practice for Obstetrics and Gynecology

“…Validated predictive models have been developed for several women's health conditions, including cesarean delivery complications, 5 successful vaginal birth after cesarean delivery, 6 artificial intelligence–based predictions of breast cancer recurrence, 7 machine learning approaches in predicting postpartum depression, 8 and postpartum hemorrhage, 9 among others. However, the pathway to clinical implementation of these types of prediction tools is challenging.…”