Artificial intelligence as an auxiliary tool in pediatric otitis media diagnosis

Abstract

Objectives

In order to promote the use of AI technology as the auxiliary tool in pediatric otitis media diagnosis, we use the convolutional neural networks and deep learning for image classification and disease diagnosis. We also designed a Pediatric Otitis Media Classifier to analyze and classify the images for physicians.

Methods

A pediatric otitis media classifier was designed for junior physicians (doctors who have been engaged in clinical practice for a short time) as an auxiliary diagnostic tool. To design this classifier for children with otitis media, we used a large number of images of acute otitis media (AOM), secretory otitis media (OME), and normal otoscope images to obtain the optimal convolutional neural network model.

Results

The average recognition accuracies of the ZFNet and the TSL16 for classification were 97.87 % and 97.62 %, far exceeding the accuracy of human diagnosis. The results of using the Pediatric Otitis Media Classifier show that we can use the classifier to correctly identify the image types of child middle ear infections.

Conclusions

We developed the Pediatric Otitis Media Classifier for the successful automated classification of AOM and OME in children using otoscopic images. In contrast to the traditional diagnosis of pediatric otitis media, which relies heavily on the experience of doctors, the diagnostic accuracy of even experienced physicians is only approximately 80 %. With AI technology, we can improve the accuracy rate to over 98 %, which can effectively assist doctors in auxiliary diagnosis. It also reduces delayed treatment, antibiotic misuse, and unnecessary surgery caused by misdiagnosis.