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

NCT07261618 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 800

Last updated 2025-12-03

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

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.

Sponsors & Collaborators

  • Sanliurfa Mehmet Akif Inan Education and Research Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
45 Years
Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-10-15
Primary Completion
2025-11-30
Completion
2025-11-30

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07261618 on ClinicalTrials.gov