Fetal AnomaliesAIRFRAMENCT06675266
AIRFRAME: Artificial Intelligence for Recognition of Fetal bRain AnoMaliEs at Second Trimester Fetal Brain Scan
An observational study of Fetal Anomalies and Brain Malformation, run by Fondazione Policlinico Universitario Agostino Gemelli IRCCS at 1 site in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 60 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Fetal Anomalies, Brain Malformation
- What is being tested?
- Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scan
- How many people
- 10,000 participants
- Where
- 1 siteItaly
- Study dates
- 30 Apr 2023to 30 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 60 years
- Women only
- Healthy volunteers accepted
- Adult
Study population: All pregnant women undergoing second trimester screening scan in the participating centers who provide informed consent to enrolment
Who may be able to join
- Women with singleton pregnancies undergoing ultrasound examination between 19+0 - 22+6 weeks of gestation
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Italy1 site, 1 recruiting
- Recruiting
Fondazione Policlinico Universitario Agostino Gemelli
Rome, 00136
Alessandra Familiari, MD, +39 3285887422, alessandra.familiari@policlinicogemelli.it
About this study
Official title: 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.
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 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.
- Conditions
- Fetal Anomalies, Brain Malformation
- Being tested
- Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scan
- Keywords
- Fetal Brain Anomaly, Brain Malformation, Second trimester ultrasound Scan
- Medical subject headings
- Congenital Abnormalities
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scanDiagnostic test
Also called Artificial Intelligence, Second trimester fetal scan
Development of AI algorithm for early detection of fetal brain anomalies in the second trimester of pregnancy
| Study group | Role | What they receive |
|---|---|---|
| CASE | Fetuses with brain anomalies Receives: Diagnostic Test: Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scan | |
| CONTROLS | Fetuses with brain anomaly Receives: Diagnostic Test: Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scan |
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 posted5 Nov 2024
- Study start30 Apr 2023
- Primary completion30 Dec 2024 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Fetal AnomaliesStudies of the same condition, open and under way.
- More trials run by Fondazione Policlinico Universitario Agostino Gemelli IRCCSThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Other
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Ospedale Di Venere - Carbonara di Bari - Bari, Italy, Azienda Ospedaliero-Universitaria di Parma
- Responsible party
- Sponsor
- Investigators
- Alessandra Familiari, Principal investigator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06675266
- Sponsor's protocol ID
- Prod. ID 4813
- Acronym
- AIRFRAME
- Status last verified
- Nov 2024
- First submitted
- 4 Nov 2024
- Last updated
- 5 Nov 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 28371793Yu 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.
PubMed 28534800Yaqub 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.
PubMed 28958729Ambroise 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.
PubMed 30177447Rydberg 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.
PubMed 27714775Hendricks 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.
PubMed 10369506Klusmann 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.
PubMed 16303691De 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.
PubMed 14745929
Study at a glance
From the registry record, last updated by the sponsor on 5 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of an Artificial Intelligence Algorithm to Recognize Abnormal Findings at Routine Fetal Brain Ultrasound. AIRFRAME (Artificial Intelligence for Recognition of Fetal bRain AnoMaliEs)
- ClinicalTrials.gov ID
- NCT06675266
- Acronym / sponsor ID
- AIRFRAME / Prod. ID 4813
- Conditions
- Fetal Anomalies, Brain Malformation
- Keywords
- Fetal Brain Anomaly, Brain Malformation, Second trimester ultrasound Scan
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Fondazione Policlinico Universitario Agostino Gemelli IRCCSUniversity, hospital or other
- Collaborators
- Ospedale Di Venere - Carbonara di Bari - Bari, Italy, Azienda Ospedaliero-Universitaria di Parma
- Study start
- 30 Apr 2023 (actual)
- Primary completion
- 30 Dec 2024 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 5 Nov 2024 (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.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| CASE | – | Diagnostic Test: Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scanFetuses with brain anomalies |
| CONTROLS | – | Diagnostic Test: Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scanFetuses with brain anomaly |
Who may qualify
- Ages
- 18 to 60 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- All pregnant women undergoing second trimester screening scan in the participating centers who provide informed consent to enrolment
Key inclusion criteria (1 listed)
- Women with singleton pregnancies undergoing ultrasound examination between 19+0 - 22+6 weeks of gestation
Key exclusion criteria (3 listed)
- 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.
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 |
|---|---|---|
| Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scanAlso: Artificial Intelligence, Second trimester fetal scanDevelopment of AI algorithm for early detection of fetal brain anomalies in the second 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
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Nov 2024.
- Results publication: PubMed 28371793Chen 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.
- Results publication: PubMed 28534800Yu 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.
- Results publication: PubMed 28958729Yaqub 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.
- Results publication: PubMed 30177447Ambroise 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.
- Results publication: PubMed 27714775Rydberg 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.
- Results publication: PubMed 10369506Hendricks 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.
- Results publication: PubMed 16303691Klusmann 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.
- Results publication: PubMed 14745929De 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.
- Individual participant dataSharing planned: Undecided
Related trials for Fetal Anomalies
RecruitingPhase 1
Serial Amnioinfusions as Regenerative Therapy for Pulmonary Hypoplasia
Renal Failure Congenital, Congenital Renal Anomaly Nos
Enrolling by invitationObservational study
North American Fetal Therapy Network Prospective Registry for Long-term Outcome Following Fetoscopic Endoluminal Tracheal Occlusion in Severe Left and Right Congenital Diaphragmatic Hernia
Congenital Diaphragmatic Hernia
Recruiting
Fetal Repair of Complex Gastroschisis: A Safety and Feasibility Trial
Gastroschisis
RecruitingObservational study
Growing Little PEAPODS Study
Premature Birth, Premature Infant
Recruiting
STV Analysis Versus Visual Evaluation of Cardiotocography in FGR
Fetal Growth Retardation
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.
Alessandra Familiari principal investigator
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
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 Anomalies 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: looking for participants now, as of the sponsor's last update on 5 Nov 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Alessandra Familiari, MD (+39 3285887422, alessandra.familiari@policlinicogemelli.it), 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 NCT06675266, which also lists 8 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 5 Nov 2024.