Artificial intelligence to classify ear disease from otoscopy: A
systematic review and meta-analysis
Abstract
Objective: To summarize the accuracy of artificial intelligence (AI)
computer vision algorithms to classify ear disease from otoscopy.
Methods: Using the PRISMA guidelines, nine online databases were
searched for articles that used AI methods (convolutional neural
networks, artificial neural networks, support vector machines, decision
trees, k-nearest neighbors) 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 images compared to
otolaryngologists (ground-truth). The Quality Assessment of Diagnostic
Accuracy Studies Version 2 tool was used to assess the quality of
methodology and risk of bias. Results: Thirty-nine articles were
included. Algorithms achieved 90.7% (95%CI: 90.1 – 91.3%) accuracy
to difference between normal or abnormal otoscopy images in 14 studies.
The most common multi-classification algorithm (3 or more diagnostic
classes) achieved 97.6% (95%CI: 97.3.- 97.9%) accuracy to
differentiate between normal, AOM and OME in 3 studies. Compared to
manual classification, AI algorithms outperformed human assessors to
classify otoscopy images achieving 93.4% (95%CI: 90.5 – 96.4%)
versus 73.2% (95%CI: 67.9 – 78.5%) accuracy in 3 studies.
Convolutional neural networks achieved the highest accuracy compared to
other classification methods. Conclusion: AI can classify ear disease
from otoscopy. A concerted effort is required to establish a
comprehensive and reliable otoscopy database for algorithm training. An
AI-supported otoscopy system may assist health care workers, trainees,
and primary care practitioners with less otology experience identify ear
disease.