aceUBNet-Seg cardiomegaly screening accuracy on external NIH and OpenI datasets
Source article: AI-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…
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Cardio surgery 3 by Dominique Cappronnier. CC BY-SA 3.0 · https://creativecommons.org/licenses/by-sa/3.0
A peer-reviewed study published July 16, 2026 describes UBNet-Seg, a lightweight 2.3-million-parameter U-Net variant that infers cardiomegaly from lung field geometry rather than explicit heart segmentation. Trained on 11,748 images, it was evaluated on external NIH and OpenI chest X-ray datasets, reporting 95.85% lung Dice and 0.05-second inference.
The work matters because it demonstrates a low-latency, explainable triage alternative for resource-constrained settings, while showing that domain shift still reduces fully automated performance on OpenI to 76.07% before human-in-the-loop correction lifts it to 91.21%. Whether the proxy approach generalizes to broader clinical populations and real workflow remains to be tested prospectively.
- Developed UBNet-Seg, a lightweight U-Net variant (2.3 million parameters) trained on 11,748 images to segment lung fields as geometric proxy, bypassing explicit heart segmentation.
- External validation on NIH and OpenI showed lung Dice 95.85% and inference time 0.05 s, with fully automated accuracy 90.31% on NIH and 76.07% on OpenI.
- Human-in-the-Loop refinement neutralized domain shifts, with McNemar test confirming improvements to 93.63% and 91.21% (p<0.001).
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.
The rundown
Researchers trained UBNet-Seg, a 2.3 million parameter U-Net variant, on 11,748 heterogeneous chest images to segment lung fields as a proxy for heart size, implementing implicit morphological inference to avoid explicit heart segmentation.
Tested on unseen NIH and OpenI datasets, the model reported 95.85% lung Dice and 0.05 s inference, with automated accuracy of 90.31% and 76.07%; adding expert-guided refinement raised accuracy to 93.63% and 91.21% respectively, with p<0.001 on McNemar test.
Fully automated performance degraded substantially on external OpenI data, requiring expert-guided refinement to restore accuracy, indicating residual domain-shift vulnerability.
Sources
- Peer-reviewedJournal of X-Ray Science and Technology2026-07-16
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