Prediction of in-vitro fertilization outcome by ultrasound strain analysis and machine learning: A multi-center study
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.
Authors
- Lin Xu (ORCID: https://orcid.org/0000-0001-5777-1496)
- C.O. Rees (ORCID: https://orcid.org/0000-0001-7742-3050)
- Nikos Christoforidis
- Yizhou Huang (ORCID: https://orcid.org/0009-0000-1584-233X)
- Dick B.C. Schoot
- Massimo Mischi
- Anyi Cheng
- Celine Blank
Institutions
- Radboud University Nijmegen (NL)
- Catharina Ziekenhuis (NL)
- ShanghaiTech University (CN)
- GZA Ziekenhuizen Campus Sint-Augustinus (BE)
- Eindhoven University of Technology (NL)
Publication Details
- Journal
- The Ultrasound Journal
- Published
- 2026-02-24
- DOI
- https://doi.org/10.5826/tuj.2026.18257
- Primary Topic
- Ovarian function and disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China