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Patients aged\u2009&gt;\u200918\u00a0years, admitted the treatment of pneumonia between March 2020 and August 2021 were included. We developed prognostic models, including an AI-based consolidation score in addition to the conventional CURB-65 (confusion, urea, respiratory rate, blood pressure, and age\u2009\u2265\u200965) and pneumonia severity index (PSI) for predicting pneumonia outcomes, defined as 30-day mortality during admission. A total of 489 patients, including 310 and 179 patients in training and test sets, were included. In the training set, the AI-based consolidation score on CXR was a significant variable for predicting the outcome (hazard ratio 1.016, 95% confidence interval [CI] 1.001\u20131.031). The model that combined CURB-65, initial O<jats:sub>2<\/jats:sub> requirement, intubation, and the AI-based consolidation score showed a significantly high C-index of 0.692 (95% CI 0.628\u20130.757) compared to other models. In the test set, this model also demonstrated a significantly high C-index of 0.726 (95% CI 0.644\u20130.809) compared to the conventional CURB-65 and PSI (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001 and 0.017, respectively). Therefore, a new prognostic model incorporating AI-based CXR results along with traditional pneumonia severity score could be a simple and useful tool for predicting pneumonia outcomes in clinical practice.<\/jats:p>","DOI":"10.1038\/s41598-024-65488-1","type":"journal-article","created":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T17:01:47Z","timestamp":1719075707000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results"],"prefix":"10.1038","volume":"14","author":[{"given":"Hyun Joo","family":"Shin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun Hye","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyunghwa","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leeha","family":"Ryu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun-Kyung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"65488_CR1","unstructured":"Korea, S. 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