The annual World Health Organization (WHO) report highlights a staggering statistic of 140 million childbirths occurring worldwide each year with a death rate of approximately 300 000. A significant contributing factor is complicated obstructed labor arising when the baby fails to navigate and negotiate the birth canal despite normal uterine contractions. Therefore, understanding of this complex fundamental physiological process plays an essential role to improve the diagnosis, optimize the clinical intervention and define predictive and preventive strategies. To study the movement of the fetal body through the birth canal, the first discovery journey of the human being, computational childbirth modeling and simulation play an essential role due to limited experimental observation (i.e. childbirth MRI). With the development of modern AI technologies (e.g. deep learning), computational childbirth modeling and simulation have been metamorphosed. In this talk, we will explore our recent researches ranging from automatic fetus segmentation to real-time soft tissue deformation during the vaginal delivery using different deep learning approaches [1-2]. In particular, advanced musculoskeletal modeling was coupled with mixed reality technology to develop the next-generation childbirth training simulator [3-4]. For the first time, the movement of the fetal body through the birth canal during birth is realistically described, simulated, and evaluated. This outcome pays the way to translate the childbirth virtual human twin to the routine practices toward predicting delivery complications and associated preventive practices. REFERENCES [1] HD Le-Nguyen et al. Generative Adversarial Network for Newborn 3D Skeleton Part Segmentation. Applied Intelligence 54, 4319–4333, 2024 [2] DH Nguyen-Le et al. A Novel Deep Learning-Driven Approach for Predicting the Pelvis Soft-Tissue Deformations toward a Real-Time Interactive Childbirth Simulation. Engineering Applications of Artificial Intelligence 126D (2023), 107150, 2024 [3] A Ballit et al. Novel Hybrid Rigid-Deformable Fetal Modeling for Simulating the Vaginal Delivery within the Second Stage of Labor. Computer Methods and Programs in Biomedicine, vol. 250: 108168, 2024 [4] A Ballit et al. Fast Soft-Tissue Deformations coupled with Mixed Reality toward the Next-generation Childbirth Training Simulator. Medical & Biological Engineering & Computing 61:2207–2226, 2023