Advanced Clinical Analysis Based on Automated Cardiothoracic Ratio Estimation Using Attention-Gated Deep Learning

Chest X-rays (CXR) are essential for diagnosing various chest conditions, including pneumonia, congestive heart failure, and cardiomegaly. Cardiomegaly is clinically defined by a Cardiothoracic Ratio (CTR) exceeding 0.5. Traditional CTR assessment relies on manual measurement, which increases radiologists’ workload and susceptibility to inter-observer variability. This study proposes a segmentation framework integrating the Efficient and Lightweight U-Net (ELU-Net) with the Convolutional Block Attention Module (CBAM). Performance was benchmarked against standard U-Net and several advanced variants with attention mechanisms. To assess clinical translatability, the proposed model was validated on an external, independent dataset of 1240 images from a private clinic (AdelaideMRI), comparing automated CTR values with expert manual assessment. The proposed method achieved superior segmentation performance, recording a DSC of 95.04%, IOU of 92.96%, and F1-score of 96.39%, outperforming all comparative architectures. In the external clinical validation, the method demonstrated a strong correlation with expert measurements (Pearson coefficient = 0.853) and a low Mean Absolute Error (MAE) of 0.0070. The system correctly classified 97% of cardiomegaly cases (CTR > 0.5). The proposed method significantly enhances the segmentation accuracy of cardiothoracic structures, demonstrating high reliability and generalizability when applied to diverse, real-world clinical data.