Development of a Deep Learning Algorithm for Posterior Fossa Abnormality Recognition on First-Trimester US Screening Scans: AIRFRAME Study Part 1

Radiology Artificial Intelligence 2026

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Authors: Alessandra Familiari, C. Di Ilio, Andrea Dall’Asta, E. Corno, Ruben Ramirez Zegarra, E. di Pasquo, Tiziana Fanelli, M. Minopoli, Basky Thilaganathan, C. Scala, Federico Prefumo, Ricciarda Raffaelli
+13 more Alessandra Bovino, E. Quarello, Julia Binder, Veronica Falcone, Gianpaolo Grisolia, Jayshree Ramkrishna, Simon Meagher, Huong Elena Tran, Carlotta Bizzarri, Marica Vagni, Luca Boldrini, Paolo Volpe, Tullio Ghi
DOI: 10.1148/ryai.250394
Cited by: 2
References: 27

Abstract

A deep learning model accurately analyzed first-trimester screening US posterior fossa images for early detection of open spina bifida and cystic posterior fossa anomalies in a multicenter cohort.

Fields of Study & Subjects

Fetal and Pediatric Neurological Disorders Prenatal Screening and Diagnostics Neonatal and fetal brain pathology

Publication details

Journal / source
Radiology Artificial Intelligence
Publisher
Radiological Society of North America (RSNA)
Volume
8
Issue
2
Pages
e250394–e250394
Publication date
21 January 2026
Language
EN