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

2026/01/21 by Alessandra Familiari, C. Di Ilio, Andrea Dall’Asta +22 · 1 voice · 2 citations
Medicine · #Fetal and Pediatric Neurological Disorders #Neonatal and fetal brain pathology #Prenatal Screening and Diagnostics

paper · doi:10.1148/ryai.250394

openalex publication_date 2026/01/21 · openalex created_date 2026/01/22 · openalex updated_date 2026/07/13

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.

Cited by

• Fetal Brain in a Box: AIRFRAME Model to Classify Brain Abnormalities at US
• Fetal Brain in B-Mode: AIRFRAME Model Takes on Posterior Fossa Malformation Detection

Discussions

• AIRFRAME part 1: developing a deep-learning algorithm for identifying posterior fossa abnormalities on first-trimester US screen scans https://doi.org/10.1148/ryai.250394 @fprefumo.bsky.social #DeepLe [bsky, 4 points, 0 comments]

Related

• Development of an Integrated Deep Learning Approach for Detecting Fetal Brain Abnormalities at Routine Second Trimester US Scan: A Multicenter Study
• Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging
• Deep Learning Model for Real-Time Nuchal Translucency Assessment at Prenatal US
• Deep Learning Analysis of Prenatal Ultrasound for Identification of Ventriculomegaly
• Prenatal assessment of brain malformations on neuroimaging: an expert panel review