{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T20:03:06Z","timestamp":1790107386068,"version":"4.0.1"},"reference-count":36,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"UB Research Grant","award":["00067.03\/UN10.A0507\/B\/ KS\/2024"],"award-info":[{"award-number":["00067.03\/UN10.A0507\/B\/ KS\/2024"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of X-Ray Science and Technology"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:p>\n                    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\u2005s. 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 (\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mi>p<\/mml:mi>\n                        <mml:mspace width=\"0.25em\"\/>\n                        <mml:mo>&lt;<\/mml:mo>\n                        <mml:mspace width=\"0.25em\"\/>\n                        <mml:mn>0.001<\/mml:mn>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ). 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.\n                  <\/jats:p>","DOI":"10.1177\/08953996261468686","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T05:39:29Z","timestamp":1784266769000},"page":"652-668","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-assisted cardiomegaly screening via implicit morphological inference and human-in-the-loop validation"],"prefix":"10.1177","volume":"34","author":[{"given":"Muhammad Masdar","family":"Mahasin","sequence":"first","affiliation":[{"name":"Department of Physics, Brawijaya University, Malang, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1216-4198","authenticated-orcid":false,"given":"Agus","family":"Naba","sequence":"additional","affiliation":[{"name":"Department of Physics, Brawijaya University, Malang, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chomsin S","family":"Widodo","sequence":"additional","affiliation":[{"name":"Department of Physics, Brawijaya University, Malang, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyun Yueniwati","family":"P. 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