AI-Assisted cardiomegaly screening via implicit morphological inference and human-in-the-loop validation

Abstract

Cardiomegaly screening via manual Cardiothoracic Ratio (CTR) measurement remains a clinical bottleneck, while contemporary deep learning solutions often suffer from algorithmic bloating. To address the need for resource-efficient and interpretable triage, this study proposes a framework driven by implicit morphological inference, which bypasses the requirement for explicit heart segmentation. We developed UBNet-Seg, a lightweight U-Net variant (2.3 million parameters) trained on a heterogeneous dataset of 11,748 images to segment lung fields as a geometric proxy. Performance was rigorously evaluated across unseen domains using external NIH and OpenI datasets. The model achieved a lung Dice Coefficient of 95.85% and an inference time of 0.05 s. Fully automated accuracy reached 90.31% on NIH and 76.07% on OpenI. Crucially, the integration of a Human-in-the-Loop mechanism successfully neutralized domain shifts; the McNemar test confirmed that expert-guided refinement provided statistically significant accuracy improvements to 93.63% and 91.21% on NIH and OpenI, respectively ( $p<0.001$ ). By validating that medial lung boundaries serve as reliable structural proxies, this framework offers a robust, explainable, and low-latency alternative suitable for resource-constrained clinical settings.