{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T16:14:03Z","timestamp":1790612043611,"version":"4.1.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:00:00Z","timestamp":1705449600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:00:00Z","timestamp":1705449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      To build and merge a diagnostic model called multi-input DenseNet fused with clinical features (MI-DenseCFNet) for discriminating between\n                      <jats:italic>Staphylococcus aureus<\/jats:italic>\n                      pneumonia (SAP) and\n                      <jats:italic>Aspergillus<\/jats:italic>\n                      pneumonia (ASP) and to evaluate the significant correlation of each clinical feature in determining these two types of pneumonia using a random forest dichotomous diagnosis model. This will enhance diagnostic accuracy and efficiency in distinguishing between SAP and ASP.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, 60 patients with clinically confirmed SAP and ASP, who were admitted to four large tertiary hospitals in Kunming, China, were included. Thoracic high-resolution CT lung windows of all patients were extracted from the picture archiving and communication system, and the corresponding clinical data of each patient were collected.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The MI-DenseCFNet diagnosis model demonstrates an internal validation set with an area under the curve (AUC) of 0.92. Its external validation set demonstrates an AUC of 0.83. The model requires only 10.24s to generate a categorical diagnosis and produce results from 20 cases of data. Compared with high-, mid-, and low-ranking radiologists, the model achieves accuracies of 78% vs. 75% vs. 60% vs. 40%. Eleven significant clinical features were screened by the random forest dichotomous diagnosis model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The MI-DenseCFNet multimodal diagnosis model can effectively diagnose SAP and ASP, and its diagnostic performance significantly exceeds that of junior radiologists. The 11 important clinical features were screened in the constructed random forest dichotomous diagnostic model, providing a reference for clinicians.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical relevance statement<\/jats:title>\n                    <jats:p>\n                      MI-DenseCFNet could provide diagnostic assistance for primary hospitals that do not have advanced radiologists, enabling patients with suspected infections like\n                      <jats:italic>Staphylococcus aureus<\/jats:italic>\n                      pneumonia or\n                      <jats:italic>Aspergillus<\/jats:italic>\n                      pneumonia to receive a quicker diagnosis and cut down on the abuse of antibiotics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key points<\/jats:title>\n                    <jats:p>\n                      <jats:italic>\u2022 MI-DenseCFNet combines deep learning neural networks with crucial clinical features to discern between Staphylococcus aureus pneumonia and Aspergillus pneumonia.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:italic>\u2022 The comprehensive group had an area under the curve of 0.92, surpassing the proficiency of junior radiologists.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:italic>\u2022 This model can enhance a primary radiologist\u2019s diagnostic capacity.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-023-10578-3","type":"journal-article","created":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:03:10Z","timestamp":1705449790000},"page":"5066-5076","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["MI-DenseCFNet: deep learning\u2013based multimodal diagnosis models for Aureus and Aspergillus pneumonia"],"prefix":"10.1007","volume":"34","author":[{"given":"Tong","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng-hua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi-hao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing-zhao","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-shu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue-mei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9217-7430","authenticated-orcid":false,"given":"Jian-qing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,17]]},"reference":[{"key":"10578_CR1","doi-asserted-by":"publisher","unstructured":"GBD 2019 LRI Collaborators (2022) Age-sex differences in the global burden of lower respiratory infections and risk. factors, 1990-2019: results from the Global Burden of Disease Study 2019. 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According to Chinese legislation and system requirements, the ethical review committee of the First Affiliated Hospital of Kunming Medical University approved and exempted written informed consent for this retrospective study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors confirm that the journal\u2019s ethical policy, as indicated on the journal\u2019s author guidelines page, has been followed and that the appropriate ethics review has obtained approval from the First Affiliated Hospital of Kunming Medical University ethics committee.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 retrospective\u2022 multicenter study","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}