PREDICTING AND MANAGING DYSTOCIA THROUGH THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE CHILDBIRTH PROCESS
Keywords:
Artificial intelligence, dystocia, childbirth, machine learning, labor management, predictive modeling, intrapartum ultrasonography, AIDA algorithm, cesarean delivery, obstetric careAbstract
Dystocia, or difficult labor, remains a significant challenge in obstetrics, contributing to increased maternal and neonatal morbidity. This thesis explores the integration of artificial intelligence (AI) in predicting and managing dystocia during childbirth. By leveraging machine learning algorithms and intrapartum ultrasonography, AI tools such as the Artificial Intelligence Dystocia Algorithm (AIDA) enable real-time risk stratification, personalized decision-making, and reduction in unnecessary interventions. A hypothetical model was developed using multimodal data, including fetal biometrics, maternal anthropometrics, and psychological factors, achieving high predictive accuracy (AUC > 0.85). The study demonstrates AI's potential to enhance labor outcomes, with recommendations for clinical integration and further validation.
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