Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta-analysis.

ABSTRACT:

Objectives

To summarise the accuracy of artificial intelligence (AI) computer vision algorithms to classify ear disease from otoscopy.

Design

Systematic review and meta-analysis.

Methods

Using the PRISMA guidelines, nine online databases were searched for articles that used AI computer vision algorithms developed from various methods (convolutional neural networks, artificial neural networks, support vector machines, decision trees and k-nearest neighbours) to classify otoscopic images. Diagnostic classes of interest: normal tympanic membrane, acute otitis media (AOM), otitis media with effusion (OME), chronic otitis media (COM) with or without perforation, cholesteatoma and canal obstruction.

Main outcome measures

Accuracy to correctly classify otoscopic image

SUBMITTER: Habib AR 

PROVIDER: S-EPMC9310803 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature