Skip to main content
OpenTrials
Recruiting

NCT Number: NCT06675266

AIRFRAME: Artificial Intelligence for Recognition of Fetal bRain AnoMaliEs at Second Trimester Fetal Brain Scan

Obstetric ultrasound represents the standard of care for the screening of the fetal anomalies. However, its performance is dependent upon several parameters including type of anomaly, gestational age, maternal habitus and skills of the examiner. The use of Artificial Intelligence (AI) in medical diagnostics has been suggested not only to reduce the inter- and intra-operator variability, but also to compress the required time necessary to perform routine tasks, hence optimizing healthcare resources. Fetal brain abnormalities are among the most challenging fetal congenital anomalies in terms of ultrasound diagnosis, prenatal counseling and management. The access to new sources of technology, i.e. AI, has the potential to improve recognition, detection and localization of brain malformations. Therefore, we propose to develop an AI-based software, which would be capable to recognize the brain structures at antenatal ultrasound and discriminate between normal and abnormal fetal brain anatomy through fully automatic data processing.

Recruiting

Interested in participating?

Request Info

Key information

Age range

18 year–60 year

Sex eligibility

Female

Study type

Observational

Primary location

Fondazione Policlinico Universitario Agostino Gemelli

Rome, 00136, Italy

Location status: Recruiting

Location contact

Alessandra Familiari, MD

CONTACT

[email protected]

+39 3285887422

About this study

The application of AI in obstetric ultrasound includes three aspects: structure identification, automatic and standardized measurements, and classification diagnosis. Since obstetric ultrasound is time-consuming, the use of AI could also reduce examination time and improve workflow.

Study design: this is a multicenter retrospective observational cohort study and subsequent prospective cohort study. The study design will be organized in two different phases.

The first phase, the feasibility retrospective study, has the objective to develop and train AI-Algorithm with normal and abnormal images retrospectively acquired during second trimester ultrasound scan from various international fetal medicine centers.

The second phase, a prospective clinical validation, has the objective to test the AI-Algorithm in the assessment of basic fetal brain anatomy in a real clinic setting with real patients from each of the participating fetal medicine centers.

Setting: Three (3) fetal medicine centers.

Participants: singleton pregnant population who underwent ultrasound examination between 19 - 22 weeks of gestation in the participating centers.

Primary endpoint: to validate a novel AI-based technology for the automated assessment of the basic anatomy of the fetal brain which could potentially be used to support second trimester screening scan.

Secondary endpoints:

To improve the performance of the standard second trimester screening of fetal brain anatomy ensuring its reliable sonographic assessment within a shorter time of execution.

To detect higher repeatability and reproducibility, allowing to implement the ultrasound screening also in terms of efficiency on a vast scale, optimizing healthcare resources In the first phase of the study, participating fetal medicine centers will search their electronic databases for images of singleton pregnant women who underwent ultrasound imaging at 19+0 - 22+6 weeks of gestation with any fetal brain anomaly. Normal images of the fetal brain at the same gestational age will be provided by the promoting centers - i.e., Fondazione Policlinico A. Gemelli, IRCCS and University of Parma. Clinical, ultrasound, prenatal and postnatal information of each case will be retrieved from patient's medical records and entered an electronic database collection file by the principal investigator from each participating center. The acquired images will be anonymized, saved as DICOM and shared through a dedicated cloud storage system which will be set up by the bioengineering team. Each center will be able to access the web system using a personal ID and password.

In the second phase of the study, the algorithm will be prospectively tested and validated in a real clinical setting with real patients from each of the participating fetal medicine centers. Inclusion and exclusion criteria, imaging protocol and data collection will be the same carried out during the retrospective phase.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Women with singleton pregnancies undergoing ultrasound examination between 19+0 - 22+6 weeks of gestation

Exclusion criteria

  • Women who did not have the second trimester screening scan at the settled gestational age.
  • Women in which a good visualization of the transventricular, transthalamic and transcerebellar plane of the fetal head was not technically possible.
  • Women who are not able to give the informed consent.

Treatment and study plan

Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scan

Diagnostic Test

Development of AI algorithm for early detection of fetal brain anomalies in the second trimester of pregnancy

Other names: Artificial Intelligence, Second trimester fetal scan

Primary outcomes

  1. AI algorithm

    Time frame: 2 years

    Number of cases detected with AI algorithm application

Secondary outcomes

  1. Reproducibility

    Time frame: 1 year

    Number of cases detected with AI algorithm application compared with those detected with standard techniques of prenatal diagnosis

Study contacts

Contact information is provided by the study sponsor or research team.

Alessandra Familiari, MD

CONTACT

[email protected]

+39 3285887422

Sponsors and collaborators

Lead sponsor

Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Other

Collaborators

  • Azienda Ospedaliero-Universitaria di Parma
  • Ospedale Di Venere - Carbonara di Bari - Bari, Italy

Registry information

Official study title

Development of an Artificial Intelligence Algorithm to Recognize Abnormal Findings at Routine Fetal Brain Ultrasound. AIRFRAME (Artificial Intelligence for Recognition of Fetal bRain AnoMaliEs)

Acronym: AIRFRAME

Important dates

Study start
2023
Primary completion
2024
Study completion
2026
First posted
Nov 5, 2024
Registry last updated
Nov 5, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.