Research to fuel the development of groundbreaking technologies

The Role of Artificial Intelligence in Screening and Democratizing Quality Prenatal Care: A Retrospective Validation on Sonograms of Fetal Brain

A Deep Learning System for the Automated Quantification and Screening of Suspected Ventriculomegaly from 2D Ultrasound Images of the Fetal Brain

Feasibility and Validation of an Artificial Intelligence Based Software to Mimic a Clinically Relevant Approach for Anatomical Assessment of 2D Fetal Neurosonogram Video Loops - A Retrospective Multi-Reader Study

Assessment of the Clinical and Workflow Benefits of an Artificial Intelligence Based System for the Semi-Automated Caliper Placement and Measurements of Fetal Neurosonogram - A Multi-Reader Feasibility Study

Towards A Device-Independent Deep Learning Approach for the Automated Segmentation of Sonographic Fetal Brain Structures: A Multi-Center and Multi-Device Validation

Leveraging Clinically Relevant Biometric Constraints to Supervise a Deep Learning Model for the Accurate Caliper Placement to Obtain Sonographic Measurements of the Fetal Brain

A Deep Learning System for the Automated Calliper Placement to Measure Multiple Fetal Brain Structures from Two-Dimensional Ultrasound Images

A Multi-centre, Multi-Device Validation of a Deep Learning System for the Automated Segmentation of Fetal Brain Structures from Two-Dimensional Ultrasound Images

Artificial Intelligence System (AIS) to Automatically Obtain Multiple Key Sonographic Measurements of the Fetal Brain in the Axial Views: A Validation Study

A Clinically-Interpretable Artificial Intelligence Based System to Automatically Detect “Lemon Sign” on Fetal Cranial Sonograms: A Multi-Center Retrospective Validation Study

Development and Validation of an Artificial Intelligence Based System for the Automated Detection of Choroid Plexus Cyst from Fetal Cranial Sonograms: A Multi-Center Retrospective Study