Advanced Fetal Cardiac Disease Detection Using Optimized Gegenbauer Graph Neural Networks on Ultrasound Images to Facilitate Early Diagnosis and Clinical Assessment

Abstract

Early detection of fetal cardiac diseases can dramatically improve neonatal outcomes by enabling timely intervention and informed clinical management. However, accurate diagnosis remains challenging due to the complexity of fetal heart structures in ultrasound images and the subtlety of congenital anomalies. To address these challenges, this work introduces Fetal Cardiac Disease Detection Using Ultrasound Imaging with Gegenbauer Graph Neural Networks (FCD-UI-GGNN). Ultrasound images are collected from the Fetal Phantom Ultrasound Dataset 23 (FPUS23) and pre-processed using Broad Collaborative Filtering (BCF) to resize images while preserving critical anatomical details. Fast Continual Multi-View Clustering (FCMVC) segments target vessel structures, and Gegenbauer Graph Neural Networks (GGNN) detects cardiac anomalies by modeling both local and global vessel relationships. The network weights are optimized using the Bitterling Fish Optimization Algorithm (BFOA) to improve accuracy. The framework is evaluated utilizing Accuracy, Precision, F1-Score, Recall, and ROC analysis, achieving 98.78% Accuracy, 99.01% Recall, 98.36% Precision, and 99.12% F1-Score. Validation on additional datasets, including FPUS23 and 4D Fetal Cardiac Ultrasound images, confirms robust generalization. These results demonstrate highly reliable and precise detection, supporting early clinical intervention for fetal cardiac anomalies.