Fetal AnomalyAIRFRAMENCT05790473
Artificial Intelligence Algorithm for the Screening of Abnormal Fetal Brain Findings at First Trimester Ultrasound Scan
An observational study of Fetal Anomaly and Brain Malformation, run by Fondazione Policlinico Universitario Agostino Gemelli IRCCS at 1 site in Italy.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 45 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Fetal Anomaly, Brain Malformation
- What is being tested?
- Artificial Intelligence
- How many people
- 10,000 participants
- Where
- 1 siteItaly
- Study dates
- 1 May 2023to 1 May 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 45 years
- Women only
- Healthy volunteers accepted
- Adult
Study population: All pregnancies undergoing first trimester screening
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Italy1 site, 1 recruiting
- Recruiting
FP Gemelli IRCCS
Rome, 00168
Alessandra Familiari, 3285887422, familiari.ale@gmail.com
About this study
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.
Read the 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 first trimester ultrasound scan from ten international fetal medicine centers.
The second phase, a prospective clinical validation, has the objective to test the AI-Algorithm in the assessment of the fetal posterior fossa 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 11 - 14 weeks of gestation in ten fetal medicine centers.
Primary endpoint: To validate a novel AI-based technology, which could potentially be used as a screening tool for fetal brain abnormal findings in the first trimester.
Secondary endpoints: To improve the performance of the standard first trimester screening of fetal posterior fossa 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 midsagittal images of singleton pregnant women who underwent ultrasound imaging at 11+0 - 13+6 weeks of gestation with any fetal posterior fossa anomaly, such as open spinal dysraphism, DWM or BPC. Normal images of the fetal posterior fossa 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.
- Conditions
- Fetal Anomaly, Brain Malformation
- Being tested
- Artificial Intelligence
- Medical subject headings
- Congenital Abnormalities
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial IntelligenceDiagnostic test
Development of AI algorithm for early detection of fetal brain anomalies in the first trimester of pregnancy
| Study group | Role | What they receive |
|---|---|---|
| Case | Fetuses with brain anomalies Receives: Diagnostic Test: Artificial Intelligence | |
| Controls | Normal with normal brain Receives: Diagnostic Test: Artificial Intelligence |
What is being measured
The primary outcome is the main question the study is designed to answer.
AI algorithm
Number of cases detected with AI algorithm application
Measured: 2 years
Secondary outcomes (1)
Reproducibility
Number of cases detected with AI algorithm application compared with those detected with standard techniques of prenatal diagnosis
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Mar 2023
- Study start1 May 2023
- Primary completion1 May 2024 (estimated)
- Study completion1 May 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Fetal AnomalyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS
- Sponsor type
- University, hospital or other
- Collaborators
- Ministero della Salute, Italy, Azienda Ospedaliero-Universitaria di Parma, Ospedale Di Venere, ASL BA, Bari Italy
- Responsible party
- Principal investigator, Familiari Alessandra, Fondazione Policlinico Universitario Agostino Gemelli IRCCS
- Investigators
- Alessandra Familiari, MD, Principal investigator, Fondazione Policlinico Agostino Gemelli
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05790473
- Sponsor's protocol ID
- 5526
- Acronym
- AIRFRAME
- Status last verified
- Mar 2024
- First submitted
- 6 Mar 2023
- Last updated
- 22 Mar 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: DICOM images through a drive among the centres
Publications
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.
PubMed 31113257Chaoui 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.
PubMed 19705402Drukker 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.
PubMed 32530098Chen 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.
PubMed 34513890
Study at a glance
From the registry record, last updated by the sponsor on 22 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Algorithm for the Screening of Abnormal Fetal Brain Findings at First Trimester Ultrasound Scan
- ClinicalTrials.gov ID
- NCT05790473
- Acronym / sponsor ID
- AIRFRAME / 5526
- Conditions
- Fetal Anomaly, Brain Malformation
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Fondazione Policlinico Universitario Agostino Gemelli IRCCSUniversity, hospital or other
- Collaborators
- Ministero della Salute, Italy, Azienda Ospedaliero-Universitaria di Parma, Ospedale Di Venere, ASL BA, Bari Italy
- Study start
- 1 May 2023 (actual)
- Primary completion
- 1 May 2024 (estimated)
- Study completion
- 1 May 2025 (estimated)
- First posted
- 30 Mar 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 first trimester ultrasound scan from ten international fetal medicine centers.
The second phase, a prospective clinical validation, has the objective to test the AI-Algorithm in the assessment of the fetal posterior fossa 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 11 - 14 weeks of gestation in ten fetal medicine centers.
Primary endpoint: To validate a novel AI-based technology, which could potentially be used as a screening tool for fetal brain abnormal findings in the first trimester.
Secondary endpoints: To improve the performance of the standard first trimester screening of fetal posterior fossa 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 midsagittal images of singleton pregnant women who underwent ultrasound imaging at 11+0 - 13+6 weeks of gestation with any fetal posterior fossa anomaly, such as open spinal dysraphism, DWM or BPC. Normal images of the fetal posterior fossa 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 design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Case | – | Diagnostic Test: Artificial IntelligenceFetuses with brain anomalies |
| Controls | – | Diagnostic Test: Artificial IntelligenceNormal with normal brain |
Who may qualify
- Ages
- 18 to 45 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- All pregnancies undergoing first trimester screening
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial IntelligenceDevelopment of AI algorithm for early detection of fetal brain anomalies in the first trimester of pregnancy | Diagnostic test | Case, Controls |
Primary outcome
AI algorithm
Time frame: 2 years
Number of cases detected with AI algorithm application
1 secondary outcome
Reproducibility
Time frame: 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Mar 2024.
- Background: PubMed 31113257Garcia-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.
- Background: PubMed 19705402Chaoui 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.
- Background: PubMed 32530098Drukker 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.
- Background: PubMed 34513890Chen 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.
- Individual participant dataSharing planned: Yes. DICOM images through a drive among the centres
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Alessandra Familiari, MD principal investigator
Fondazione Policlinico Agostino Gemelli
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Fetal Anomaly and Brain Malformation, run by Fondazione Policlinico Universitario Agostino Gemelli IRCCS at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 22 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Alessandra Familiari, MD (3285887422, familiari.ale@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AI algorithm (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05790473, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Mar 2024.