Trial reportFrontiers in public health2025
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.
Trial report in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Progressive exacerbation of neurological symptoms in a patient with neurosyphilis following T9-T10 intradural space-occupying lesion resection: a case report.Frontiers in immunology · 2026Article
- Self-management and role of nurses of diabetic patients: a critical narrative literature review during COVID-19 pandemic.Frontiers in medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Influenza-related global deaths reach 650,000 annually. The current highly lethal clinical subtype of influenza is severe influenza. Aim: To develop and validate a deep learning based model for early diagnosis of severe influenza. Methods: This 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. Significance: Our 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 dissemination: This 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 number: https://clinicaltrials.gov/, identifier (ChiCTR2000028883).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.