Prediction of CSF Intervention in Fetal Ventriculomegaly via Artificial Intelligence–Powered Normative Modeling
This work introduces a deep learning-based method for fast and accurate quantification of ventricular volumes in fetal brain MRI. A normative reference standard derived by using this method can predict VM and a need for postnatal CSF intervention. Increased ventricular volume is a strong predictor of postnatal intervention.
Authors
- Elizabeth George (ORCID: https://orcid.org/0000-0002-1274-7836)
- Siddharthasiva A Rajan
- Andreas M. Rauschecker (ORCID: https://orcid.org/0000-0003-0633-9876)
- Juana Barrera Bayona
- Dawn Gano (ORCID: https://orcid.org/0000-0003-4192-2362)
- Pierre Nedelec (ORCID: https://orcid.org/0000-0002-8535-1541)
- Orit A. Glenn
- Nalin Gupta (ORCID: https://orcid.org/0000-0001-9539-7052)
- Mingxi Zhou (ORCID: https://orcid.org/0000-0002-9244-4527)
Institutions
- University of California, San Francisco (US)
- University of California System (US)
- University of San Francisco (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-09-16
- DOI
- https://doi.org/10.3174/ajnr.a9000
- Primary Topic
- Fetal and Pediatric Neurological Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00