{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:23Z","timestamp":1780963223655,"version":"3.54.1"},"reference-count":13,"publisher":"Georg Thieme Verlag KG","issue":"CP","license":[{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Ultrasound Int Open"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>To develop and validate an end-to-end interactive software that integrates\n                        lung ultrasound radiomics and machine learning for the objective assessment\n                        of pediatric pneumonia severity at the point of care.<\/jats:p>\n                  <jats:p>This retrospective study included a dataset of lung ultrasound images from\n                        293 pediatric patients (157 with mild pneumonia and 136 with severe\n                        pneumonia). A total of 104 radiomics features were extracted from\n                        clinician-delineated regions of interest. Feature selection was performed\n                        using Least Absolute Shrinkage and Selection Operator regression, and 10\n                        machine learning algorithms were constructed and evaluated. The optimal\n                        model was interpreted using SHapley Additive exPlanations. The finalized\n                        classifier was integrated into an interactive software platform that guides\n                        users from image upload to severity prediction.<\/jats:p>\n                  <jats:p>The Light Gradient Boosting Machine classifier, which forms the core of the\n                        software, demonstrated superior performance on the independent test set,\n                        achieving an accuracy of 89.8%, a sensitivity of 91.7%, a specificity of\n                        88.6%, and an area under the curve of 90.1%. SHapley Additive exPlanation\n                        analysis identified and ranked the contribution of key predictive features,\n                        with morphological characteristics being the most influential.<\/jats:p>\n                  <jats:p>We present an end-to-end interactive software that successfully leverages\n                        lung ultrasound radiomics and machine learning to provide an objective,\n                        accurate, and rapid assessment of pediatric pneumonia severity. This tool\n                        has significant potential to standardize diagnosis and support clinical\n                        decision-making in real-world settings.<\/jats:p>","DOI":"10.1055\/a-2879-4395","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T23:26:44Z","timestamp":1780961204000},"source":"Crossref","is-referenced-by-count":0,"special_numbering":"S","title":["An End-to-end Interactive Software for Pediatric Pneumonia Severity\n                    Assessment from Lung Ultrasound Images"],"prefix":"10.1055","volume":"12","author":[{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Chaoyang Hospital, Capital Medical\n                    University, China","place":["Beijing"],"department":["Department of Ultrasound Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing Chaoyang Hospital, Capital Medical\n                    University, China","place":["Beijing"],"department":["Department of Pediatrics"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Jiang","sequence":"additional","affiliation":[{"name":"Beijing Chaoyang Hospital, Capital Medical\n                    University, China","place":["Beijing"],"department":["Department of Pediatrics"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingling","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Chaoyang Hospital, Capital Medical\n                    University, China","place":["Beijing"],"department":["Department of Ultrasound Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qili","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Chaoyang Hospital, Capital Medical\n                    University, China","place":["Beijing"],"department":["Department of Ultrasound Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0161-3311","authenticated-orcid":false,"given":"Zufei","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Chaoyang Hospital, Capital Medical\n                    University, China","place":["Beijing"],"department":["Department of Otorhinolaryngology, Head and Neck Surgery"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"issue":"03","key":"ref2","first-page":"689","article-title":"Lung ultrasound for diagnosing pneumonia","volume":"161","author":"G Volpicelli","year":"2022","journal-title":"Chest"},{"issue":"04","key":"ref3","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s00134-012-2513-4","article-title":"International evidence-based recommendations for point-of-care lung\n                    ultrasound","volume":"38","author":"G Volpicelli","year":"2012","journal-title":"Intensive Care Med"},{"key":"ref4","first-page":"1","article-title":"Emergency thoracic ultrasound in the differentiation of the etiology of\n                    shortness of breath (ETUDES)","volume":"12","author":"A S Liteplo","year":"2020","journal-title":"Crit Ultrasound J"},{"issue":"04","key":"ref5","first-page":"656","article-title":"Lung ultrasound in COVID-19 and beyond","volume":"10","author":"S Mongodi","year":"2021","journal-title":"J Clin Med"},{"issue":"05","key":"ref6","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1097\/CCM.0000000000005303","article-title":"Extended lung ultrasound to differentiate between pneumonia and atelectasis in\n                    critically ill patients: A diagnostic accuracy study","volume":"50","author":"M E Haaksma","year":"2022","journal-title":"Crit Care Med"},{"issue":"02","key":"ref7","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1002\/jum.16088","article-title":"New international guidelines and consensus on the use of lung ultrasound","volume":"42","author":"L Demi","year":"2023","journal-title":"J Ultrasound Med"},{"issue":"04","key":"ref8","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1038\/s41390-021-01928-2","article-title":"A training plan to implement lung ultrasound for diagnosing pneumonia in\n                    children","volume":"92","author":"C Guitart","year":"2022","journal-title":"Pediatr Res"},{"issue":"21","key":"ref9","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"JJ M Griethuysen","year":"2017","journal-title":"Cancer Res"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1164\/rccm.202008-3131OC","article-title":"Lung ultrasound to monitor extremely preterm infants and predict\n                    bronchopulmonary dysplasia. A multicenter longitudinal cohort study","volume":"203","author":"B Loi","year":"2021","journal-title":"Am J Respir Crit Care Med"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"3107","DOI":"10.3390\/jcm13113107","article-title":"Diagnostic accuracy of lung ultrasound in neonatal diseases: A systematised\n                    review","volume":"13","author":"S Nobile","year":"2024","journal-title":"J Clin Med"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.3390\/children10111753","article-title":"Noninvasive monitoring strategies for bronchopulmonary dysplasia or\n                    post-prematurity respiratory disease: Current challenges and future\n                    prospects","volume":"10","author":"T Zini","year":"2023","journal-title":"Children"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"e2862","DOI":"10.1055\/s-0043-1775975","article-title":"Prognostic relevance of the lung ultrasound score: A multioutcome study in\n                    infants with respiratory distress syndrome","volume":"41","author":"P Szyma\u0144ski","year":"2024","journal-title":"Am J Perinatol"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/s41575-021-00523-4","article-title":"Advancing the global public health agenda for NAFLD: A consensus statement","volume":"19","author":"J V Lazarus","year":"2022","journal-title":"Nat Rev Gastroenterol Hepatol"}],"container-title":["Ultrasound International Open"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-2879-4395.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T23:26:48Z","timestamp":1780961208000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-2879-4395"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,8]]},"references-count":13,"journal-issue":{"issue":"CP","published-online":{"date-parts":[[2026,4,8]]},"published-print":{"date-parts":[[2026]]}},"alternative-id":["a28794395"],"URL":"https:\/\/doi.org\/10.1055\/a-2879-4395","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["2509-596X","2199-7152"],"issn-type":[{"value":"2509-596X","type":"print"},{"value":"2199-7152","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,8]]}}}