aceAI-Assisted cardiomegaly screening via implicit morphological inference and human-in-the-loop validation
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…
UBNet-Seg, a 2.3M-parameter U-Net variant using lung fields as geometric proxy, achieved 95.85% lung Dice at 0.05s inference and 90.31% automated cardiomegaly accuracy on NIH, rising to 93.63% on NIH and 91.21% on OpenI after expert-guided refinement.
Fully automated cardiomegaly screening accuracy dropped to 76.07% on the external OpenI dataset, showing domain-shift vulnerability, while manual CTR measurement remains a clinical bottleneck and existing deep models suffer from algorithmic bloating.
Fully automated performance degraded substantially on external OpenI data, requiring expert-guided refinement to restore accuracy, indicating residual domain-shift vulnerability.
Evidence
- Peer-reviewedJournal of X-Ray Science and Technology2026-07-16
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Truvace Impact Record TRV-2026-0257, v1: “AI-Assisted cardiomegaly screening via implicit morphological inference and human-in-the-loop validation.” Truvace, 2026-07-18. /record/TRV-2026-0257 (accessed at citation time). sha256 5db808dd1b3d2b98…
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