ArticleJournal of medical Internet research2022
Examining the Use of an Artificial Intelligence Model to Diagnose Influenza: Development and Validation Study.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- A 3-Tier AI Model for COVID-19 Triage Using Pharyngeal Images: Algorithm Development and Validation.JMIR formative research · 2026Article
- Clinical artificial intelligence (AI) in Japan: Regulatory pathways, domain-specific evidence, and its data infrastructure from an international perspective.Global health & medicine · 2026Review
- Infectious disease diagnosis by artificial intelligence (AI): Differences in patient backgrounds and symptoms between antigen test positives and novel AI-powered pharyngeal endoscopy test positives.PLOS digital health · 2026Article
- Influenza virus research in an era of emerging respiratory threats: host-virus interactions, surveillance technologies, antiviral management and resistance, and vaccine preparedness.Frontiers in microbiology · 2026Review
- High performance COVID-19 screening using machine learning.La Tunisie medicale · 2025Article
- Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan.BMJ health & care informatics · 2024Article
- Identifying sex from pharyngeal images using deep learning algorithm.Scientific reports · 2024Article
- Preliminary External Validation Results of the Artificial Intelligence-Based Headache Diagnostic Model: A Multicenter Prospective Observational Study.Life (Basel, Switzerland) · 2024Article
- The peculiar characteristics and advancement in diagnostic methodologies of influenza A virus.Frontiers in microbiology · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe global burden of influenza is substantial. It is a major disease that causes annual epidemics and occasionally, pandemics. Given that influenza primarily infects the upper respiratory system, it may be possible to diagnose influenza infection by applying deep learning to pharyngeal images.
objectiveWe aimed to develop a deep learning model to diagnose influenza infection using pharyngeal images and clinical information.
methodsWe recruited patients who visited clinics and hospitals because of influenza-like symptoms. In the training stage, we developed a diagnostic prediction artificial intelligence (AI) model based on deep learning to predict polymerase chain reaction (PCR)-confirmed influenza from pharyngeal images and clinical information. In the validation stage, we assessed the diagnostic performance of the AI model. In additional analysis, we compared the diagnostic performance of the AI model with that of 3 physicians and interpreted the AI model using importance heat maps.
resultsWe enrolled a total of 7831 patients at 64 hospitals between November 1, 2019, and January 21, 2020, in the training stage and 659 patients (including 196 patients with PCR-confirmed influenza) at 11 hospitals between January 25, 2020, and March 13, 2020, in the validation stage. The area under the receiver operating characteristic curve for the AI model was 0.90 (95% CI 0.87-0.93), and its sensitivity and specificity were 76% (70%-82%) and 88% (85%-91%), respectively, outperforming 3 physicians. In the importance heat maps, the AI model often focused on follicles on the posterior pharyngeal wall.
conclusionsWe developed the first AI model that can accurately diagnose influenza from pharyngeal images, which has the potential to help physicians to make a timely diagnosis.
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