{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:28:13Z","timestamp":1775471293838,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T00:00:00Z","timestamp":1738886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2013Portugal","award":["UIDB\/00645\/2020"],"award-info":[{"award-number":["UIDB\/00645\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2013Portugal","award":["UIDP\/00408\/2020"],"award-info":[{"award-number":["UIDP\/00408\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2013Portugal","award":["UIDB\/00408\/2020"],"award-info":[{"award-number":["UIDB\/00408\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2013Portugal","award":["DSAIPA\/AI\/0083\/2020"],"award-info":[{"award-number":["DSAIPA\/AI\/0083\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2013Portugal","award":["2022.15319.BD"],"award-info":[{"award-number":["2022.15319.BD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The admission of COVID-19 patients to the Intensive Care Unit (ICU) is largely dependent on illness severity, yet no standard criteria exist for this decision. Here, lung ultrasound (LU) data, blood gas analysis (BGA), and clinical parameters from venous blood tests (VBTs) were used, along with machine-learning (ML) models to predict the need for ICU admission. Data from fifty-one COVID-19 patients, including ICU admission status, were collected. The information from LU was gathered through the identification of LU findings (LUFs): B-lines, irregular pleura, subpleural, and lobar consolidations. LU scores (LUSs) were computed by summing predefined weights assigned to each LUF, as reported in previous studies. In addition, individual LUFs were analyzed without calculating a total LUS. Support vector machine models were built, combining the available clinical data to predict ICU admissions. The application of ML models to individual LUFs outperformed standard LUS approaches reported in previous studies. Moreover, combining LU data with results from other medical exams improved the area under the receiver operating characteristic curve (AUC). The model with the best overall performance used variables from all three exams (BGA, LU, VBT), achieving an AUC of 95.5%. Overall, the results demonstrate the significant role of ML models in improving the prediction of ICU admission. Additionally, applying ML specifically to LUFs provided better results compared to traditional approaches that rely on traditional LUSs. The results of this paper are deployed on a web app.<\/jats:p>","DOI":"10.3390\/jimaging11020045","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T05:04:33Z","timestamp":1738904673000},"page":"45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["We Need to Talk About Lung Ultrasound Score: Prediction of Intensive Care Unit Admission with Machine Learning"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3631-1786","authenticated-orcid":false,"given":"Duarte","family":"Oliveira-Saraiva","sequence":"first","affiliation":[{"name":"Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Faculdade de Ci\u00eancias da Universidade de Lisboa, 1749-016 Lisboa, Portugal"},{"name":"LASIGE, Faculdade de Ci\u00eancias da Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9659-8438","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Leote","sequence":"additional","affiliation":[{"name":"Critical Department, Hospital Garcia de Orta E.P.E, 2805-267 Almada, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8539-1092","authenticated-orcid":false,"given":"Filipe Andr\u00e9","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"Critical Department, Hospital Garcia de Orta E.P.E, 2805-267 Almada, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6371-3310","authenticated-orcid":false,"given":"Nuno Cruz","family":"Garcia","sequence":"additional","affiliation":[{"name":"LASIGE, Faculdade de Ci\u00eancias da Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4323-3942","authenticated-orcid":false,"given":"Hugo Alexandre","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Faculdade de Ci\u00eancias da Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13089-022-00278-2","article-title":"Time course of lung ultrasound findings in patients with COVID-19 pneumonia and cardiac dysfunction","volume":"14","author":"Leote","year":"2022","journal-title":"Ultrasound J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1186\/s13054-020-03240-7","article-title":"Incidence of ARDS and outcomes in hospitalized patients with COVID-19: A global literature survey","volume":"24","author":"Tzotzos","year":"2020","journal-title":"Crit. Care"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s40560-023-00658-3","article-title":"Guideline-based management of acute respiratory failure and acute respiratory distress syndrome","volume":"11","author":"Fujishima","year":"2023","journal-title":"J. Intensive Care"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s13054-023-04368-y","article-title":"A structured diagnostic algorithm for patients with ARDS","volume":"27","author":"Bos","year":"2023","journal-title":"Crit. Care"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1001\/jama.2016.0291","article-title":"Epidemiology, Patterns of Care, and Mortality for Patients with Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries","volume":"315","author":"Bellani","year":"2016","journal-title":"JAMA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1001\/jamainternmed.2020.2033","article-title":"Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19","volume":"180","author":"Liang","year":"2020","journal-title":"JAMA Intern. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1111\/echo.14962","article-title":"The COVID-19 Worsening Score (COWS)\u2014A predictive bedside tool for critical illness","volume":"38","author":"Boero","year":"2021","journal-title":"Echocardiography"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1136\/emermed-2016-205937","article-title":"Lung ultrasound: A useful tool in the assessment of the dyspnoeic patient in the emergency department. Fact or fiction?","volume":"35","author":"Wimalasena","year":"2018","journal-title":"Emerg. Med. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13613-020-00799-w","article-title":"Lung ultrasound score predicts outcomes in COVID-19 patients admitted to the emergency department","volume":"11","author":"Marchini","year":"2021","journal-title":"Ann. Intensive Care"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.4187\/respcare.08648","article-title":"Lung Ultrasound Score to Predict Outcomes in COVID-19","volume":"66","author":"Aso","year":"2021","journal-title":"Respir. Care"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"00128-2022","DOI":"10.1183\/23120541.00128-2022","article-title":"Focused lung ultrasound to predict respiratory failure in patients with symptoms of COVID-19: A multicentre prospective cohort study","volume":"8","author":"Skaarup","year":"2022","journal-title":"ERJ Open Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Stecher, S.S., Anton, S., Fraccaroli, A., G\u00f6tschke, J., Stemmler, H.J., and Barnikel, M. (2021). Lung ultrasound predicts clinical course but not outcome in COVID-19 ICU patients: A retrospective single-center analysis. BMC Anesthesiol., 21.","DOI":"10.1186\/s12871-021-01396-5"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1186\/s13054-020-03416-1","article-title":"Prognostic value of bedside lung ultrasound score in patients with COVID-19","volume":"24","author":"Ji","year":"2020","journal-title":"Crit. Care"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dargent, A., Chatelain, E., Kreitmann, L., Quenot, J.P., Cour, M., Argaud, L., Antoine, M., Baudry, T., Bertrand, P.J., and Bougnaud, J. (2020). Lung ultrasound score to monitor COVID-19 pneumonia progression in patients with ARDS. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0236312"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s10479-022-04984-x","article-title":"Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients","volume":"328","author":"Saadatmand","year":"2023","journal-title":"Ann. Oper. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Altini, N., Brunetti, A., Mazzoleni, S., Moncelli, F., Zagaria, I., Prencipe, B., Lorusso, E., Buonamico, E., Carpagnano, G.E., and Bavaro, D.F. (2021). Predictive machine learning models and survival analysis for covid-19 prognosis based on hematochemical parameters. Sensors, 21.","DOI":"10.3390\/s21248503"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Podder, P., and Mondal, M.R.H. (2020, January 17\u201319). Machine learning to predict COVID-19 and ICU requirement. Proceedings of the 2020 11th International Conference on Electrical and Computer Engineering, ICECE 2020, Dhaka, Bangladesh.","DOI":"10.1109\/ICECE51571.2020.9393123"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aznar-Gimeno, R., Esteban, L.M., Labata-Lezaun, G., Del-Hoyo-alonso, R., Abadia-Gallego, D., Pa\u00f1o-Pardo, J.R., Esquillor-Rodrigo, M.J., Lanas, \u00c1., and Serrano, M.T. (2021). A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18168677"},{"key":"ref_19","first-page":"26","article-title":"Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization","volume":"17","author":"Wu","year":"2019","journal-title":"J. Electron. Sci. Technol."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/2\/45\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:28:34Z","timestamp":1760027314000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/2\/45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,7]]},"references-count":19,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["jimaging11020045"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11020045","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,7]]}}}