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.
Hearing outcomes after mobile ear surgery for chronic otitis media in Greenland
