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

NCT06675266 | Recruiting

• 1 other identifier: Prod. ID 4813
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Fetal Anomalies; Brain Malformation

Keywords

Fetal Brain Anomaly; Brain Malformation; Second trimester ultrasound Scan

Outcome Measures

Primary Outcomes (1)

• AI algorithm

  Number of cases detected with AI algorithm application

  2 years

Secondary Outcomes (1)

• Reproducibility

  1 year

Study Arms (2)

CASE

Fetuses with brain anomalies

Diagnostic Test: Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening 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

Interventions

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

Also known as: Artificial Intelligence, Second trimester fetal scan

CASE; CONTROLS

Eligibility Criteria

• Age: 18 Years - 60 Years
• Gender Eligibility Details: Pregnant women
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• 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

You may qualify if:

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

You may not qualify if:

• 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.

Sponsors & Collaborators

• Fondazione Policlinico Universitario Agostino Gemelli IRCCS: lead
• Ospedale Di Venere - Carbonara di Bari - Bari, Italy: collaborator
• Azienda Ospedaliero-Universitaria di Parma: collaborator

Study Sites (1)

Fondazione Policlinico Universitario Agostino Gemelli

Rome, 00136, Italy

RECRUITING

Related Publications (8)

• Chen H, Wu L, Dou Q, Qin J, Li S, Cheng JZ, Ni D, Heng PA. Ultrasound Standard Plane Detection Using a Composite Neural Network Framework. IEEE Trans Cybern. 2017 Jun;47(6):1576-1586. doi: 10.1109/TCYB.2017.2685080. Epub 2017 Mar 30.

  PMID: 28371793 RESULT

• Yu Z, Tan EL, Ni D, Qin J, Chen S, Li S, Lei B, Wang T. A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition. IEEE J Biomed Health Inform. 2018 May;22(3):874-885. doi: 10.1109/JBHI.2017.2705031. Epub 2017 May 17.

  PMID: 28534800 RESULT

• Yaqub M, Kelly B, Papageorghiou AT, Noble JA. A Deep Learning Solution for Automatic Fetal Neurosonographic Diagnostic Plane Verification Using Clinical Standard Constraints. Ultrasound Med Biol. 2017 Dec;43(12):2925-2933. doi: 10.1016/j.ultrasmedbio.2017.07.013. Epub 2017 Sep 28.

  PMID: 28958729 RESULT

• Ambroise Grandjean G, Hossu G, Bertholdt C, Noble P, Morel O, Grange G. Artificial intelligence assistance for fetal head biometry: Assessment of automated measurement software. Diagn Interv Imaging. 2018 Nov;99(11):709-716. doi: 10.1016/j.diii.2018.08.001. Epub 2018 Sep 1.

  PMID: 30177447 RESULT

• Rydberg C, Tunon K. Detection of fetal abnormalities by second-trimester ultrasound screening in a non-selected population. Acta Obstet Gynecol Scand. 2017 Feb;96(2):176-182. doi: 10.1111/aogs.13037. Epub 2016 Nov 22.

  PMID: 27714775 RESULT

• Hendricks KA, Simpson JS, Larsen RD. Neural tube defects along the Texas-Mexico border, 1993-1995. Am J Epidemiol. 1999 Jun 15;149(12):1119-27. doi: 10.1093/oxfordjournals.aje.a009766.

  PMID: 10369506 RESULT

• Klusmann A, Heinrich B, Stopler H, Gartner J, Mayatepek E, Von Kries R. A decreasing rate of neural tube defects following the recommendations for periconceptional folic acid supplementation. Acta Paediatr. 2005 Nov;94(11):1538-42. doi: 10.1080/08035250500340396.

  PMID: 16303691 RESULT

• De Wals P, Rusen ID, Lee NS, Morin P, Niyonsenga T. Trend in prevalence of neural tube defects in Quebec. Birth Defects Res A Clin Mol Teratol. 2003 Nov;67(11):919-23. doi: 10.1002/bdra.10124.

  PMID: 14745929 RESULT

MeSH Terms

Conditions

Congenital Abnormalities

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Congenital, Hereditary, and Neonatal Diseases and Abnormalities

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Alessandra Familiari

  Fondazione Policlinico Universitario A. Gemelli, IRCCS

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Alessandra Familiari, MD

CONTACT

+39 3285887422; alessandra.familiari@policlinicogemelli.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 4, 2024
• First Posted: November 5, 2024
• Study Start: April 30, 2023
• Primary Completion: December 30, 2024
• Study Completion (Estimated): December 30, 2026
• Last Updated: November 5, 2024

Record last verified: 2024-11

Locations

IT (1)