Ignite Creation Date:
2025-12-25 @ 4:33 AM
Ignite Modification Date:
2025-12-26 @ 3:35 AM
Study NCT ID:
NCT07261618
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-12-03
First Post:
2025-11-21
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
AI-Assisted 2D Fetal Brain Ultrasound for Intracranial Anomaly Detection
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 800}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2025-10-15', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2025-11-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-21', 'studyFirstSubmitDate': '2025-11-21', 'studyFirstSubmitQcDate': '2025-11-21', 'lastUpdatePostDateStruct': {'date': '2025-12-03', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-12-03', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2025-11-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Diagnostic Accuracy of the AI-Assisted Model (Alyssia)', 'timeFrame': 'From study start to model validation (approximately 6 weeks).', 'description': 'The primary outcome is the diagnostic accuracy of the Alyssia artificial intelligence model in classifying archived 2D fetal brain ultrasound images as normal or abnormal. Model performance will be evaluated by comparing AI-generated classifications with expert-labeled ground truth data.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Intracranial Anomalies', 'Ultrasound Imaging', 'Artificial Intelligence']}, 'descriptionModule': {'briefSummary': "Timely detection of fetal brain anomalies is critical for improving prenatal counseling and postnatal neurological outcomes. Ultrasonography is the most commonly used and effective imaging method for evaluating fetal structures; however, diagnostic accuracy can be affected by operator experience, fetal position, and image quality, leading to variability in interpretation. Artificial intelligence (AI)-based image analysis offers a new opportunity to standardize diagnostic assessment and reduce subjectivity in ultrasound interpretation.\n\nThis study aims to evaluate the diagnostic accuracy and clinical applicability of an AI-assisted model (Alyssia) designed to analyze archived 2D fetal brain ultrasound images. The model will be trained and validated to distinguish between normal and abnormal intracranial findings, focusing particularly on the lateral ventricles and other relevant brain regions. The research employs an observational, retrospective design using anonymized ultrasound data obtained during routine prenatal examinations between 18 and 24 weeks of gestation.\n\nExpert clinicians will review and label all eligible images to establish ground truth classifications for model training and validation. A deep learning-based algorithm will be developed to automatically classify these images, and its performance will be evaluated using accuracy, sensitivity, specificity, precision, and F1-score metrics. Misclassified cases will be qualitatively analyzed to determine contributing factors such as image quality, anatomical variability, and gestational differences.\n\nBy comparing AI model outputs with expert-labeled references, the study will assess the model's ability to enhance diagnostic standardization and reduce inter-observer variability. The findings are expected to provide valuable insights into the integration of AI-based decision support systems in prenatal neurosonography. Ultimately, this research aims to support earlier and more reliable detection of fetal brain anomalies, contributing to improved prenatal care and healthier outcomes for mothers and infants."}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT'], 'maximumAge': '45 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'This study will use archived and anonymized 2D fetal brain ultrasound images obtained during routine prenatal screening examinations conducted between the 18th and 24th weeks of gestation. The dataset represents a diverse population of pregnant individuals aged 18-45 years who underwent standard obstetric ultrasound evaluations. All images were acquired as part of routine clinical care and stored in the institutional digital archive. Only diagnostically adequate images clearly displaying the lateral ventricles and other intracranial regions were included. The study population therefore consists of ultrasound records rather than direct human participants, ensuring complete anonymity and protection of personal data.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Archived 2D fetal brain ultrasound images obtained during routine prenatal examinations.\n* Gestational age between 18 and 24 weeks at the time of imaging.\n* Maternal age between 18 and 45 years.\n* Clear visualization of the lateral ventricles and other intracranial regions.\n* Images meeting diagnostic quality standards suitable for analysis.\n* Fully anonymized images with no patient identifiers.\n* Availability of expert assessment to classify each image as normal or abnormal.\n\nExclusion Criteria:\n\n* Ultrasound images with poor diagnostic quality or motion artifacts.\n* Incomplete, duplicate, or corrupted image records.\n* Ambiguous gestational age or missing clinical metadata.\n* Images containing any identifiable patient information.\n* Cases outside the specified gestational window (before 18 or after 24 weeks).\n* Images unrelated to the fetal brain (misfiled or mislabeled data).'}, 'identificationModule': {'nctId': 'NCT07261618', 'acronym': 'ALYSSIA', 'briefTitle': 'AI-Assisted 2D Fetal Brain Ultrasound for Intracranial Anomaly Detection', 'organization': {'class': 'OTHER', 'fullName': 'Sanliurfa Mehmet Akif Inan Education and Research Hospital'}, 'officialTitle': 'Evaluation of an Artificial Intelligence-Assisted Diagnostic Model for the Analysis of Archived 2D Fetal Brain Ultrasound Images to Improve Detection and Standardization of Intracranial Anomalies', 'orgStudyIdInfo': {'id': 'E-47749665-050.04-4465'}, 'secondaryIdInfos': [{'id': 'MEF University Ethics Committe', 'type': 'OTHER', 'domain': 'E-47749665-050.04-4465'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'Normal Fetal Brain Images', 'description': 'Archived 2D fetal brain ultrasound images classified as normal by expert reviewers.', 'interventionNames': ['Diagnostic Test: Alyssia - AI-Assisted Diagnostic Model for Fetal Brain Ultrasound']}, {'label': 'Abnormal Fetal Brain Images', 'description': 'Archived 2D fetal brain ultrasound images with confirmed intracranial anomalies, labeled by experts.', 'interventionNames': ['Diagnostic Test: Alyssia - AI-Assisted Diagnostic Model for Fetal Brain Ultrasound']}], 'interventions': [{'name': 'Alyssia - AI-Assisted Diagnostic Model for Fetal Brain Ultrasound', 'type': 'DIAGNOSTIC_TEST', 'description': 'Artificial intelligence-based diagnostic tool designed to classify archived 2D fetal brain ultrasound images as normal or abnormal to detect intracranial anomalies.', 'armGroupLabels': ['Abnormal Fetal Brain Images', 'Normal Fetal Brain Images']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Bursa', 'country': 'Turkey (Türkiye)', 'facility': 'Nefise nazlı Yenigül', 'geoPoint': {'lat': 40.19559, 'lon': 29.06013}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sanliurfa Mehmet Akif Inan Education and Research Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor, Obstetrics and Gynecology', 'investigatorFullName': 'Nefise Nazlı YENIGUL', 'investigatorAffiliation': 'Sanliurfa Mehmet Akif Inan Education and Research Hospital'}}}}