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

November 4, 2024 updated by: Fondazione Policlinico Universitario Agostino Gemelli IRCCS

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

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

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.

Study Type

Observational

Enrollment (Estimated)

10000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

All pregnant women undergoing second trimester screening scan in the participating centers who provide informed consent to enrolment

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
CASE
Fetuses with brain anomalies
Diagnostic test: Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scan
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
CONTROLS
Fetuses with brain anomaly
Diagnostic test: Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scan
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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
AI algorithm
Time Frame: 2 years
Number of cases detected with AI algorithm application
2 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Reproducibility
Time Frame: 1 year
Number of cases detected with AI algorithm application compared with those detected with standard techniques of prenatal diagnosis
1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Collaborators

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

Investigators

  • Principal Investigator: Alessandra Familiari, Fondazione Policlinico Universitario A. Gemelli, IRCCS

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

April 30, 2023

Primary Completion (Estimated)

December 30, 2024

Study Completion (Estimated)

December 30, 2026

Study Registration Dates

First Submitted

November 4, 2024

First Submitted That Met QC Criteria

November 4, 2024

First Posted (Estimated)

November 5, 2024

Study Record Updates

Last Update Posted (Estimated)

November 5, 2024

Last Update Submitted That Met QC Criteria

November 4, 2024

Last Verified

November 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Prod. ID 4813

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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