Machine learning prediction of live birth after IVF using the morphological uterus sonographic assessment group features of adenomyosis
Predicting live birth after the first IVF/ICSI treatment is challenging, as many factors may interactto affect IVF/ICSI outcomes. Adenomyosis is one factor that impacts live birth rates. Machinelearning algorithms have been shown valuable for detecting complex dependencies and predictingoutcomes in different clinical settings. We aimed to develop a prediction model for live birth afterIVF/ICSI trePredicting live birth after the first IVF/ICSI treatment is challenging, as many factors may interact to affect IVF/ICSI outcomes. Adenomyosis is one factor that impacts live birth rates. Machine learning algorithms have been shown valuable for detecting complex dependencies and predicting outcomes in different clinical settings. We aimed to develop a prediction model for live birth after IVF/ICSI
