Ignite Creation Date:
2025-12-24 @ 4:03 PM
Ignite Modification Date:
2025-12-24 @ 4:03 PM
Study NCT ID:
NCT06675266
Status:
RECRUITING
Last Update Posted:
2024-11-05
First Post:
2024-11-04
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
AIRFRAME: Artificial Intelligence for Recognition of Fetal bRain AnoMaliEs at Second Trimester Fetal Brain Scan
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000013', 'term': 'Congenital Abnormalities'}], 'ancestors': [{'id': 'D009358', 'term': 'Congenital, Hereditary, and Neonatal Diseases and Abnormalities'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D001185', 'term': 'Artificial Intelligence'}], 'ancestors': [{'id': 'D000465', 'term': 'Algorithms'}, {'id': 'D055641', 'term': 'Mathematical Concepts'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 10000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-04-30', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-11', 'completionDateStruct': {'date': '2026-12-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-11-04', 'studyFirstSubmitDate': '2024-11-04', 'studyFirstSubmitQcDate': '2024-11-04', 'lastUpdatePostDateStruct': {'date': '2024-11-05', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-11-05', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-12-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'AI algorithm', 'timeFrame': '2 years', 'description': 'Number of cases detected with AI algorithm application'}], 'secondaryOutcomes': [{'measure': 'Reproducibility', 'timeFrame': '1 year', 'description': 'Number of cases detected with AI algorithm application compared with those detected with standard techniques of prenatal diagnosis'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Fetal Brain Anomaly', 'Brain Malformation', 'Second trimester ultrasound Scan'], 'conditions': ['Fetal Anomalies', 'Brain Malformation']}, 'referencesModule': {'references': [{'pmid': '28371793', 'type': 'RESULT', 'citation': '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': '28534800', 'type': 'RESULT', 'citation': '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': '28958729', 'type': 'RESULT', 'citation': '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': '30177447', 'type': 'RESULT', 'citation': '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': '27714775', 'type': 'RESULT', 'citation': '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': '10369506', 'type': 'RESULT', 'citation': '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': '16303691', 'type': 'RESULT', 'citation': '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': '14745929', 'type': 'RESULT', 'citation': '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.'}]}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': "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.\n\nStudy design: this is a multicenter retrospective observational cohort study and subsequent prospective cohort study. The study design will be organized in two different phases.\n\nThe 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.\n\nThe 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.\n\nSetting: Three (3) fetal medicine centers.\n\nParticipants: singleton pregnant population who underwent ultrasound examination between 19 - 22 weeks of gestation in the participating centers.\n\nPrimary 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.\n\nSecondary endpoints:\n\nTo improve the performance of the standard second trimester screening of fetal brain anatomy ensuring its reliable sonographic assessment within a shorter time of execution.\n\nTo 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.\n\nIn 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."}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT'], 'maximumAge': '60 Years', 'minimumAge': '18 Years', 'genderBased': True, 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'All pregnant women undergoing second trimester screening scan in the participating centers who provide informed consent to enrolment', 'genderDescription': 'Pregnant women', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Women with singleton pregnancies undergoing ultrasound examination between 19+0 - 22+6 weeks of gestation\n\nExclusion Criteria:\n\n* Women who did not have the second trimester screening scan at the settled gestational age.\n* Women in which a good visualization of the transventricular, transthalamic and transcerebellar plane of the fetal head was not technically possible.\n* Women who are not able to give the informed consent.'}, 'identificationModule': {'nctId': 'NCT06675266', 'acronym': 'AIRFRAME', 'briefTitle': 'AIRFRAME: Artificial Intelligence for Recognition of Fetal bRain AnoMaliEs at Second Trimester Fetal Brain Scan', 'organization': {'class': 'OTHER', 'fullName': 'Fondazione Policlinico Universitario Agostino Gemelli IRCCS'}, 'officialTitle': 'Development of an Artificial Intelligence Algorithm to Recognize Abnormal Findings at Routine Fetal Brain Ultrasound. AIRFRAME (Artificial Intelligence for Recognition of Fetal bRain AnoMaliEs)', 'orgStudyIdInfo': {'id': 'Prod. ID 4813'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'CASE', 'description': 'Fetuses with brain anomalies', 'interventionNames': ['Diagnostic Test: Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scan']}, {'label': 'CONTROLS', 'description': 'Fetuses with brain anomaly', 'interventionNames': ['Diagnostic Test: Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scan']}], 'interventions': [{'name': 'Development of AI algorithm for early detection of fetal brain anomalies in the second trimester screening scan', 'type': 'DIAGNOSTIC_TEST', 'otherNames': ['Artificial Intelligence', 'Second trimester fetal scan'], 'description': 'Development of AI algorithm for early detection of fetal brain anomalies in the second trimester of pregnancy', 'armGroupLabels': ['CASE', 'CONTROLS']}]}, 'contactsLocationsModule': {'locations': [{'zip': '00136', 'city': 'Rome', 'status': 'RECRUITING', 'country': 'Italy', 'contacts': [{'name': 'Alessandra Familiari, MD', 'role': 'CONTACT', 'email': 'alessandra.familiari@policlinicogemelli.it', 'phone': '+39 3285887422'}], 'facility': 'Fondazione Policlinico Universitario Agostino Gemelli', 'geoPoint': {'lat': 41.89193, 'lon': 12.51133}}], 'centralContacts': [{'name': 'Alessandra Familiari, MD', 'role': 'CONTACT', 'email': 'alessandra.familiari@policlinicogemelli.it', 'phone': '+39 3285887422'}], 'overallOfficials': [{'name': 'Alessandra Familiari', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Fondazione Policlinico Universitario A. Gemelli, IRCCS'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Fondazione Policlinico Universitario Agostino Gemelli IRCCS', 'class': 'OTHER'}, 'collaborators': [{'name': 'Ospedale Di Venere - Carbonara di Bari - Bari, Italy', 'class': 'UNKNOWN'}, {'name': 'Azienda Ospedaliero-Universitaria di Parma', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}