Artificial Intelligence Enabled Prenatal Exam

Origin Medical EXAM ASSISTANTTM assists a wide range of users across various clinical settings to deliver standardized and quality prenatal pregnancy examinations.
"Tablet displaying a medical ultrasound scan with detailed annotations, highlighting various cardiac and anatomical features for analysis, including sections labeled LVOT (Left Ventricular Outflow Tract) and RT (Right). The interface shows options for measurement and quality assessment on the right-hand panel."
Assistive AI Technology Throughout All Trimesters Scans
The AI algorithms assist in real-time anatomical surveys from routinely performed sweeps.
Assistive Technology Throughout All Trimesters Scans
The AI algorithms detect standard diagnostic views from fetal anatomy, enabling
real-time assessment of the quality, appropriateness, and completeness of the exam.
Assistive Technology Throughout All Trimesters Scans
The AI algorithms facilitate automated assessment and ensure reproducible measurements in real-time.

Key Benefits

Standardize & democratize highest quality prenatal examination across all care settings
Empower sonographers with a higher degree of confidence via assistive exam technology
Improve workflow efficiencies and productivity
Strengthen reading physicians' diagnostic confidence via AI
Reduce examination time
Improve accessibility

Clinical Evidence

Clinical Validation
The Role of Artificial Intelligence in Screening and Democratizing Quality Prenatal Care: A Retrospective Validation on Sonograms of Fetal Brain
August 14, 2022
Clinical Validation
A Deep Learning System for the Automated Quantification and Screening of Suspected Ventriculomegaly from 2D Ultrasound Images of the Fetal Brain
July 17, 2022
Pilots
Feasibility and Validation of an Artificial Intelligence Based Software to Mimic a Clinically Relevant Approach for Anatomical Assessment of 2D Fetal Neurosonogram Video Loops
June 30, 2022
Pilots
Assessment of the Clinical and Workflow Benefits of an Artificial Intelligence Based System for the Semi-Automated Caliper Placement and Measurements of Fetal Neurosonogram
June 30, 2022
Clinical Validation
Towards A Device-Independent Deep Learning Approach for the Automated Segmentation of Sonographic Fetal Brain Structures: A Multi-Center and Multi-Device Validation
April 4, 2022
Clinical 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
March 31, 2022
Clinical Validation
A Deep Learning System for the Automated Calliper Placement to Measure Multiple Fetal Brain Structures from Two-Dimensional Ultrasound Images
October 17, 2021
Clinical Validation
A Multi-centre, Multi-Device Validation of a Deep Learning System for the Automated Segmentation of Fetal Brain Structures from Two-Dimensional Ultrasound Images
October 17, 2021
Clinical Validation
Artificial Intelligence System (AIS) to Automatically Obtain Multiple Key Sonographic Measurements of the Fetal Brain in the Axial Views: A Validation Study
October 3, 2021
Clinical Validation
A Clinically-Interpretable Artificial Intelligence Based System to Automatically Detect “Lemon Sign” on Fetal Cranial Sonograms: A Multi-Center Retrospective Validation Study
August 22, 2021
Clinical Validation
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
August 22, 2021
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