Mechanical Effects of a Maylard Scar During a Vaginal Birth After a Previous Caesarean

Fidalgo, D.S.; Pouca, Maria Vila; Oliveira, Dulce; Malanowska, Ewelina; Myers, Kristin M.; Jorge, R.M. Natal; Parente, Marco

doi:10.1007/s10439-021-02805-z

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…Gravidity may intricately correlate with physiological elements, such as uterine status, uterine musculature elasticity, and ligamentous tension, all of which collectively influence the likelihood of a successful vaginal birth. 38 Furthermore, our current study's SHAP analysis demonstrated that a history of successful vaginal births significantly impacts the VBAC success. Women with such a history may possess more favorable birth canals, thereby increasing the likelihood of a successful current vaginal delivery.…”

Section: Discussionmentioning

confidence: 63%

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

Yang,

Wang,

Lin

et al. 2024

DIGITAL HEALTH

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Background Vaginal birth after cesarean (VBAC) is generally regarded as a safe and viable birthing option for most women with prior cesarean delivery. Nonetheless, concerns about heightened risks of adverse maternal and perinatal outcomes have often dissuaded women from considering VBAC. This study aimed to assess the performance of an artificial intelligence (AI)-powered VBAC prediction system integrated into a decision-aid birth choice platform for shared decision-making (SDM). Materials and Methods Employing a retrospective design, we collected medical records from a regional hospital in northern Taiwan from January 2019 to May 2023. To explore a suitable model for tabular data, we compared two prevailing modeling approaches: tree-based models and logistic regression models. We subjected the tree-based algorithm, CatBoost, to binary classification. Results Forty pregnant women with 347 records were included. The CatBoost model demonstrated a robust performance, boasting an accuracy rate of 0.91 (95% confidence interval (CI): 0.86–0.94) and an area under the curve of 0.89 (95% CI: 0.86–0.93), surpassing both regression models and other boosting techniques. CatBoost captured the data characteristics on the significant impact of gravidity and the positive influence of previous vaginal birth, reinforcing established clinical guidelines, as substantiated by the SHapley Additive exPlanations analysis. Conclusion Using AI techniques offers a more accurate assessment of VBAC risks, boosting women’s confidence in selecting VBAC as a viable birthing option. The seamless integration of AI prediction systems with SDM platforms holds a promising potential for enhancing the effectiveness of clinical applications in the domain of women's healthcare.

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Section: Discussionmentioning

confidence: 63%

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

Yang,

Wang,

Lin

et al. 2024

DIGITAL HEALTH

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show abstract

“…The conference also highlighted newer clinical application areas such as reproduction and pregnancy. For instance, Fidalgo et al 4 investigated scarring due to a Cesarean section through a computational biomechanics model of delivery. Finally, in silico trials are emerging as a powerful tool as shown by Favre et al 3 who provided a perspective on in silico clinical trials for orthopedic devices, highlighting challenges and potential benefits.…”

mentioning

confidence: 99%

Special Issue of the VPH2020 Conference: “Virtual Physiological Human: When Models, Methods and Experiments Meet the Clinic”

et al. 2022

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On the uncertainty quantification of the active uterine contraction during the second stage of labor simulation

Nguyen,

Ballit,

Lecomte-Grosbras

et al. 2024

Med Biol Eng Comput

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Mechanical Effects of a Maylard Scar During a Vaginal Birth After a Previous Caesarean

Cited by 6 publications

References 39 publications

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

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

Special Issue of the VPH2020 Conference: “Virtual Physiological Human: When Models, Methods and Experiments Meet the Clinic”

On the uncertainty quantification of the active uterine contraction during the second stage of labor simulation

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