FADA: An AI-Enabled Healthcare Informatics Platform for Prenatal Ultrasound Analysis, Clinical Decision Support, and Automated Reporting

Abstract

Prenatal ultrasound examination is the primary screening tool for monitoring fetal development and identifying hereditary abnormalities before birth. Despite its widespread use, accurately interpreting fetal brain ultrasound images requires specialized expertise and considerable clinical experience. Variations in operator skill, image quality, reporting practices, and access to specialist services can influence diagnostic consistency and timeliness. These challenges highlight the need for decision-support tools that can assist clinicians in image interpretation while fitting naturally into existing clinical workflows.This paper presents the Fetal Abnormality Detection Application (FADA), an AI-enabled healthcare informatics platform designed to support fetal brain ultrasound assessment through automated image analysis, clinical decision support, interactive consultation, and report generation. The platform provides an integrated environment where healthcare professionals can upload ultrasound images, receive automated measurements and abnormality assessments, interact with an intelligent clinical assistant, and obtain structured diagnostic reports through a unified interface.At the core of FADA is a curated dataset containing more than 3,000 expert-annotated fetal brain ultrasound images. The dataset focuses on clinically important anatomical structures, including the Cavum Septum Pellucidum (CSP) and Lateral Ventricles (LV), both of which serve as important indicators of fetal neurological development. The annotated dataset forms the basis for machine learning models trained to identify relevant anatomical regions, perform measurements, and detect patterns associated with developmental abnormalities. The platform supports automated assessment of these structures and produces quantitative measurements that can assist clinicians during prenatal evaluation.The system architecture follows a modular healthcare informatics design consisting of four primary functions: user interaction, clinical data processing, intelligent analysis, and results management. Healthcare professionals interact with the platform through a web-based clinical workspace that supports image submission, consultation, and review of findings. Submitted images are processed through an AI analysis pipeline that performs anatomical identification, measurement extraction, abnormality assessment, and generation of preliminary findings. In parallel, an intelligent conversational assistant enables users to ask questions regarding imaging findings, measurements, and clinical interpretation, providing contextual guidance and explanatory information. The outputs of these services are consolidated into structured clinical summaries and downloadable reports suitable for review and incorporation into existing clinical documentation workflows.A distinguishing aspect of FADA is the integration of image interpretation and clinical reporting within a single workflow. Rather than functioning solely as a diagnostic algorithm, the platform acts as a healthcare information system that connects image acquisition, automated analysis, clinician interaction, and report generation. This approach reduces repetitive administrative tasks, promotes reporting consistency, and supports traceable documentation of imaging findings.The proposed platform demonstrates how artificial intelligence can be embedded within healthcare information systems to support routine prenatal screening and fetal assessment. By combining expert-annotated imaging data, automated anomaly detection, conversational clinical assistance, measurement extraction, and report generation, FADA provides a practical model for computer-assisted prenatal diagnostics. The platform illustrates the potential of healthcare informatics to improve the efficiency, consistency, and accessibility of fetal ultrasound assessment while preserving clinician oversight in the diagnostic process.

Keywords: Prenatal Ultrasound, Artificial Intelligence, Healthcare Informatics

DOI: 10.54941/ahfe1008115