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.

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).

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.