Artificial Intelligence Algorithm for the Screening of Abnormal Fetal Brain Findings at First Trimester Ultrasound Scan
Trial ID: NCT05790473
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-05-01
Completion date: 2025-05-01
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Collaborators: Ministero della Salute, Italy; Azienda Ospedaliero-Universitaria di Parma; Ospedale Di Venere, ASL BA, Bari Italy
Conditions: Fetal Anomaly; Brain Malformation
Interventions: Artificial Intelligence
Locations: FP Gemelli IRCCS
Countries: Italy
Enrollment: 10000
Age range: 18 Years - 45 Years
Sex: FEMALE

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.

Eligibility criteria:
Inclusion Criteria:

* 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.

Exclusion Criteria:

* 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.

Outcomes:
AI algorithm