OP11.06: The predictive value of MUSA features of adenomyosis on live birth is poor, using a machine learning algorithm.

Autor: Alson, S., Björnsson, O., Hansson, S., Henic, E., Sladkevicius, P.
Předmět:
Zdroj: Ultrasound in Obstetrics & Gynecology; Sep2024 Supplement 1, Vol. 64, p88-89, 2p
Abstrakt: This article examines the predictive value of Morphological Uterus Sonographic Assessment (MUSA) group features of adenomyosis on live birth after the first in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) treatment. The study included 1037 women aged 25-39 undergoing their first IVF/ICSI treatment. An Extreme Gradient Boosting (XGBoost) algorithm was used to develop a prediction model, which had an area under the receiver operating characteristics (ROC) curve of 0.69. The study found that a regular junctional zone (JZ) was the best ultrasonographic variable in predicting live birth, while serum antimüllerian hormone (s-AMH) was the best clinical variable. However, the predictive ability of MUSA features in relation to live birth after IVF/ICSI treatment was poor. The authors suggest that additional variables should be included in a clinically useful prediction model for IVF/ICSI treatment outcome. [Extracted from the article]
Databáze: Complementary Index