AUTHOR=Chen Yifei , Bo Yan 

TITLE=AI-driven early detection of severe influenza in Jiangsu, China: a deep learning model validated through the design of multi-center clinical trials and prospective real-world deployment

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1610244

DOI=10.3389/fpubh.2025.1610244

ISSN=2296-2565

ABSTRACT=BackgroundInfluenza-related global deaths reach 650,000 annually. The current highly lethal clinical subtype of influenza is severe influenza.AimTo develop and validate a deep learning based model for early diagnosis of severe influenza.MethodsThis is a multi-centre, double-blind, multi-stage, randomised controlled clinical trial. We initially developed a framework for a 5-phase study: model development, external validation, multi-reader study, randomised controlled trial and prospective validation. The data source for the preview programme is electronic health record data from 87 hospitals in Jiangsu Province from 2019 to 2025.SignificanceOur expected result is that the developed model of severe influenza can be more accurate and have a lower misdiagnosis rate than traditional clinical assessment. The pre-specified AUC was 0.18 (95% CI: 0.14-0.22), with an expected 32% reduction in misdiagnosis. The model’s performance was consistent across patients in older adults, underlying disease, and resource-poor areas. The added value of the study is that it is effective in improving early recognition of severe influenza.Ethics and disseminationThis study was approved by the Institutional Review Board of Yangzhou University Hospital (IRB No. YKL08-002). Written informed consent was obtained from all participants. The results of this study will be disseminated in the form of a conference in the Jiangsu Province area, which will facilitate the translation of clinical research results and provide a powerful decision-making tool for the precise prevention and control of severe influenza.Clinical trial numberhttps://clinicaltrials.gov/, identifier (ChiCTR2000028883).