{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T08:57:44Z","timestamp":1791190664514,"version":"4.1.0"},"reference-count":13,"publisher":"S. Karger AG","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neonatology"],"abstract":"<jats:p>&lt;p&gt;Introduction: The aim of this study was to develop and validate a deep learning-based multi-class classification model for automated grading of respiratory distress syndrome (RDS) severity on neonatal chest radiographs. Methods: A total of 23,210 radiographs, including normal and RDS cases, manually annotated by trained neonatologists, were divided into training, validation, and external test sets using patient-level splitting. Lung regions were segmented using UNet++, and RDS severity was classified into five ordered grades using a ResNet-50-based model. Results: The model achieved a quadratic weighted kappa of 0.696, with 85.7% of predictions within one grade and five-class accuracy of 0.575. AUROCs were 0.966 for detecting RDS and 0.866 for clinically significant RDS (grade \u22653). Gradient-weighted Class Activation Mapping demonstrated attention to lung regions with reduced aeration and granular opacities in severe RDS. Conclusions: This model may provide objective and interpretable radiographic assessment of RDS severity and support more consistent radiographic evaluation in neonatal intensive care units.&lt;\/p&gt;<\/jats:p>","DOI":"10.1159\/neo\/adsag006","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T13:04:29Z","timestamp":1786021469000},"page":"1-1","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Classification for Grading of Respiratory Distress Syndrome on Neonatal Chest Radiographs"],"prefix":"10.1159","author":[{"given":"Seung-hak","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumin","family":"Jung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun Hee","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju Sun","family":"Heo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0931-2353","authenticated-orcid":false,"given":"Byung Min","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"127","published-online":{"date-parts":[[2026,8,28]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-020-2787-9"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1136\/archdischild-2024-327068"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-11268-4"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.17.19231"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9378(78)90241-7"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s00431-016-2744-4"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1002\/ppul.24076"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1111\/apa.15028"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1159\/000513783"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging6120131"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3054390"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1111\/1754-9485.13273"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1159\/000545107"}],"container-title":["Neonatology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/karger.com\/article-pdf\/doi\/10.1159\/neo\/adsag006","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T08:47:51Z","timestamp":1791190071000},"score":1,"resource":{"primary":{"URL":"https:\/\/karger.com\/article\/doi\/10.1159\/neo\/adsag006"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,28]]},"references-count":13,"URL":"https:\/\/doi.org\/10.1159\/neo\/adsag006","archive":["Portico"],"relation":{"is-supplemented-by":[{"id-type":"doi","id":"10.6084\/m9.figshare.33196923","asserted-by":"subject"}]},"ISSN":["1661-7800","1661-7819"],"issn-type":[{"value":"1661-7800","type":"print"},{"value":"1661-7819","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,28]]}}}