AI-Assisted 2D Fetal Brain Ultrasound for Intracranial Anomaly Detection

NCT ID: NCT07261618

Last Updated: 2025-12-03

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 800 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-10-15
• Study Completion Date: 2025-11-30

Brief Summary

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.

This 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.

Expert 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.

By 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.

Conditions

Intracranial Anomalies; Ultrasound Imaging; Artificial Intelligence

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Normal Fetal Brain Images

Archived 2D fetal brain ultrasound images classified as normal by expert reviewers.

Alyssia - AI-Assisted Diagnostic Model for Fetal Brain Ultrasound

Intervention Type: DIAGNOSTIC_TEST

Artificial intelligence-based diagnostic tool designed to classify archived 2D fetal brain ultrasound images as normal or abnormal to detect intracranial anomalies.

Abnormal Fetal Brain Images

Archived 2D fetal brain ultrasound images with confirmed intracranial anomalies, labeled by experts.

Alyssia - AI-Assisted Diagnostic Model for Fetal Brain Ultrasound

Intervention Type: DIAGNOSTIC_TEST

Artificial intelligence-based diagnostic tool designed to classify archived 2D fetal brain ultrasound images as normal or abnormal to detect intracranial anomalies.

Interventions

Alyssia - AI-Assisted Diagnostic Model for Fetal Brain Ultrasound

Artificial intelligence-based diagnostic tool designed to classify archived 2D fetal brain ultrasound images as normal or abnormal to detect intracranial anomalies.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Archived 2D fetal brain ultrasound images obtained during routine prenatal examinations.
• Gestational age between 18 and 24 weeks at the time of imaging.
• Maternal age between 18 and 45 years.
• Clear visualization of the lateral ventricles and other intracranial regions.
• Images meeting diagnostic quality standards suitable for analysis.
• Fully anonymized images with no patient identifiers.
• Availability of expert assessment to classify each image as normal or abnormal.

Exclusion Criteria

• Ultrasound images with poor diagnostic quality or motion artifacts.
• Incomplete, duplicate, or corrupted image records.
• Ambiguous gestational age or missing clinical metadata.
• Images containing any identifiable patient information.
• Cases outside the specified gestational window (before 18 or after 24 weeks).
• Images unrelated to the fetal brain (misfiled or mislabeled data).

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 45 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

Sanliurfa Mehmet Akif Inan Education and Research Hospital

OTHER

Sponsor Role: lead

Responsible Party

Nefise Nazlı YENIGUL

Associate Professor, Obstetrics and Gynecology

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Nefise nazlı Yenigül

Bursa, , Turkey (Türkiye)

Countries

Turkey (Türkiye)

Other Identifiers

MEF University Ethics Committe

Identifier Type: OTHER

Identifier Source: secondary_id

E-47749665-050.04-4465

Identifier Type: -

Identifier Source: org_study_id