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Yang, T., Zhang, L., Sun, S., Yao, X., Wang, L. and Ge, Y. (2024) Identifying Severe Community-Acquired Pneumonia Using Radiomics and Clinical Data: A Machine Learning Approach. Scientific Reports, 14, Article No. 21884.
https://doi.org/10.1038/s41598-024-72310-5

has been cited by the following article:

• TITLE: Recent Advances in the Assessment Methods and Indicators for the Severity of Severe Pneumonia

  AUTHORS: Fei Li, Jun Li, Wei Xiao

  KEYWORDS: Severe Pneumonia, Severity Assessment, Biomarkers, Imaging Techniques, Artificial Intelligence

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.13 No.5, May 27, 2025

  ABSTRACT: As a fatal respiratory disease, accurate assessment of the severity of severe pneumonia is of great significance for clinical treatment and prognosis. In recent years, with the development of biomarkers, imaging technology and artificial intelligence, significant breakthroughs have been made in the assessment of severe pneumonia. This article systematically reviews the clinical scoring system of severe pneumonia, the application of new biomarkers, the innovation of imaging technology, and the role of artificial intelligence and big data, analyzes the advantages and limitations of the existing assessment methods, and looks forward to the future direction of the research, so as to provide a scientific basis for clinical practice and scientific research.