Artificial Intelligence Algorithm for the Screening of Abnormal Fetal Brain Findings at First Trimester Ultrasound Scan

NCT05790473 | Recruiting DMC

• 1 other identifier: 5526
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief Summary

Visualization of the posterior fossa brain spaces, their spatial relationship and measurements can be obtained in the midsagittal view of fetal head, the same used for NT measurement (9), and plays an important role in the early diagnosis of neural tube defects, such as open spinal dysraphism (5), and posterior fossa anomalies, such as DWM or BPC (7). However, assessment of the fetal posterior fossa in the first trimester is still challenging due to several limitations including involuntary movements of the fetus and small size of the brain structures, causing difficulties for examination and misdiagnosis. Moreover, it is also operator-dependent for the acquirement of high-quality ultrasound images, standard measurements, and precise diagnosis. The use of new technologies to improve the acquisition of images, to help automatically perform measurements, or aid in the diagnosis of fetal abnormalities, may be of great importance for the optimal assessment of the fetal brain, particularly in the first trimester (10). Artificial intelligence (AI) is described as the ability of a computer program to perform processes associated with human intelligence, such as learning, thinking and problem-solving. Deep Learning (DL), a subset of Machine Learning (ML), is a branch of AI, defined by the ability to learn features automatically from data without human intervention. In DL, the input and output are connected by multiple layers loosely modeled on the neural pathways of the human brain. In the image recognition field, one of the most promising type of DL networks is represented by convolutional neural networks (CNN). These are designed to extract highly representative image features in a fully automated way, which makes them applicable to diagnostic decision-making. According to these observations, we propose a research project aimed to develop an ultrasound-based AI-algorithm, which is capable to assess the fetal posterior fossa structures during the first trimester ultrasound scan and discriminate between normal and abnormal findings through a fully automatic data processing.

Conditions

Fetal Anomaly; Brain Malformation

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: Artificial Intelligence

Controls

Normal with normal brain

Diagnostic Test: Artificial Intelligence

Interventions

Artificial Intelligence DIAGNOSTIC_TEST

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

Case; Controls

Eligibility Criteria

• Age: 18 Years - 45 Years
• Gender Eligibility Details: pregnant women
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

All pregnancies undergoing first trimester screening

You may qualify if:

• Women with single pregnancies who underwent ultrasound examination between 11+0 - 13+6 weeks of gestation or a fetal crown-rump-length between 45 - 84 mm.

You may not qualify if:

• Women who did not have the first trimester screening scan at the settled gestational age.
• Women in which a good visualization of the mid-sagittal view 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
• Ministero della Salute, Italy: collaborator
• Azienda Ospedaliero-Universitaria di Parma: collaborator
• Ospedale Di Venere, ASL BA, Bari Italy: collaborator

Study Sites (1)

FP Gemelli IRCCS

Rome, 00168, Italy

RECRUITING

Related Publications (4)

• Garcia-Rodriguez R, Garcia-Delgado R, Romero-Requejo A, Medina-Castellano M, Garcia-Hernandez JA, Gonzalez-Martin JM, Sepulveda W. First-trimester cystic posterior fossa: reference ranges, associated findings, and pregnancy outcomes. J Matern Fetal Neonatal Med. 2021 Mar;34(6):933-942. doi: 10.1080/14767058.2019.1622673. Epub 2019 Jun 4.

  PMID: 31113257 BACKGROUND

• Chaoui R, Benoit B, Mitkowska-Wozniak H, Heling KS, Nicolaides KH. Assessment of intracranial translucency (IT) in the detection of spina bifida at the 11-13-week scan. Ultrasound Obstet Gynecol. 2009 Sep;34(3):249-52. doi: 10.1002/uog.7329.

  PMID: 19705402 BACKGROUND

• Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol. 2020 Oct;56(4):498-505. doi: 10.1002/uog.22122.

  PMID: 32530098 BACKGROUND

• Chen Z, Liu Z, Du M, Wang Z. Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications. Front Med (Lausanne). 2021 Aug 27;8:733468. doi: 10.3389/fmed.2021.733468. eCollection 2021.

  PMID: 34513890 BACKGROUND

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, MD

  Fondazione Policlinico Agostino Gemelli

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Alessandra Familiari, MD

CONTACT

3285887422; familiari.ale@gmail.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinician

Study Record Dates

• First Submitted: March 6, 2023
• First Posted: March 30, 2023
• Study Start: May 1, 2023
• Primary Completion: May 1, 2024
• Study Completion: May 1, 2025
• Last Updated: March 22, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP
• Time Frame: after 1 year and for 1 year
• Access Criteria: pathological cases

Locations

IT (1)