JMIR Publications

ABSTRACT

Background:

Influenza is a major global burden of disease, causing annual epidemics and occasional pandemics. Given that influenza primarily infects the upper respiratory system, influenza infection may be able to be diagnosed by applying deep learning to pharyngeal images.

Objective:

We aimed to develop a deep learning model to diagnose influenza infection using the data on pharyngeal images and clinical information.

Methods:

We recruited patients who visited clinics and hospitals due to 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 the additional analysis, we compared the diagnostic performance of the AI model with that of three physicians, and also interpreted the AI model using the importance heatmaps.

Results:

A total of 7,831 patients were enrolled at 64 hospitals between Nov 1, 2019 and Jan 21, 2020 in the training stage, and 659 patients (including 196 patients with PCR-confirmed influenza) at 11 hospitals between Jan 25, 2020 and Mar 13, 2020 in the validation stage. The area under the receiver operating characteristic curve of the AI model was 0.90 (95% confidence interval, 0.87–0.93), and its sensitivity and specificity were 76% (70–82%) and 88% (85–91%), respectively, outperforming three physicians. In the importance heatmaps, the AI model often focused on follicles on the posterior pharyngeal wall.

Conclusions:

We developed the first AI model that can accurately diagnose influenza from pharyngeal images, which has the potential to assist physicians make timely diagnosis.