Deep Learning for Classification of Pediatric Otitis Media
Zebin Wu1,2, Zheqi Lin3, Lan Li2
1Department of Otolaryngology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Objectives/Hypothesis:
To create a new strategy for monitoring pediatric otitis media (OM), we developed a brief, reliable, and objective method for automated classification using convolutional neural networks (CNNs) with images from otoscope.
Study Design:
Prospective study.
Methods:
An otoscopic image classifier for pediatric OM was built upon the idea of deep learning and transfer learning using the two most widely used CNN architectures named Xception and MobileNet-V2. Otoscopic images, including acute otitis media (AOM), otitis media with effusion (OME), and normal ears were obtained from our institution. Among qualified otoendoscopic images, 10,703 images were used for training, and 1,500 images were used for testing. In addition, 102 images captured by smartphone with WI-FI connected otoscope were used as a prospective test set to evaluate the model for home screening and monitoring.
Results:
For all diagnoses combined in the test set, the Xception model and the MobileNet-V2 model had similar overall accuracies of 97.45% (95% CI 96.81%-97.94%) and 95.72% (95% CI 95.12%-96.16%). The overall accuracies of two models with smartphone images were 90.66% (95% CI 90.21%-90.98%) and 88.56% (95% CI 87.86%-90.05%). The class activation map results showed that the extracted features of smartphone images were the same as those of otoendoscopic images.
Conclusions:
We have developed deep learning algorithms for the successfully automated classification of pediatric AOM and OME with otoscopic images. With a smartphone-enabled wireless otoscope, artificial intelligence may assist parents in early detection and continuous monitoring at home to decrease the visit frequencies.
Level Of Evidence:
NA Laryngoscope, 131:E2344-E2351, 2021.
Related Concept Videos
01:17Classification of Illness
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
01:30Classification of Leukocytes
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
01:30Classification of Signals
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
01:22Force Classification
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
01:29Suctioning the Nasopharyngeal Airway
Equipment Required
01:25Suctioning the Oropharyngeal Airway
After assembling the equipment, the nurse should practice hand hygiene and don appropriate PPE according to infection control guidelines to avoid the...