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Updated: Feb 26, 2026
A Novel Use of Three-dimensional High-frequency Ultrasonography for Early Pregnancy Characterization in the Mouse
Published on: October 24, 2017
Predicción del resultado de la fertilización in vitro mediante análisis de deformación por ultrasonido y aprendizaje
Anyi Cheng1, Yizhou Huang2, Connie Rees3
1School of Information Science and Technology, ShanghaiTech University, Shanghai, China; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands; Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China.
Background:
In-vitro-fertilization (IVF) failure rates remain above 65% with unknown causes. Uterine receptivity, largely determined by uterine peristalsis, is believed to play a key role in the IVF success. Accurate assessment of uterine peristalsis holds the potential for improving the success rate of embryo implantation.
Methods:
This prospective study includes 62 IVF patients from multiple fertility centers under three different clinical settings. Four-minute B-mode transvaginal ultrasound (TVUS) scans were performed one hour before embryo transfer (ET). 25 features related to frequency, amplitude, power, velocity, and coordination were extracted using strain analysis from TVUS speckle tracking results. Three probabilistic classifiers, i.e., support vector machine (SVM), K-nearest neighbors (KNN), and adaptive boosting (AdaBoost), were employed to discriminate uterine activity as either favorable or adverse to clinical pregnancy rate. Prior to machine learning, feature selection was performed by categorized feature ranking and sequential forward selection. The proposed method was evaluated by a nested 8-fold cross validation. Results: Our results suggest that features related to coordination and frequency of the uterine peristalsis are strongly associated with clinical pregnancy. SVM demonstrates the best classification performance between successful and unsuccessful pregnancies, with an average area under the ROC curve of 0.81.
Conclusions:
We developed a machine learning framework to improve the prediction of IVF outcome based on multi-center TVUS recordings. Our SVM model identified significant uterine motion features and demonstrated reliable and generalizable classification performance. This work can provide useful means to support clinicians for clinical decision-making prior to ET and possibly enhance IVF success rates.
