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

Al Rahim Habib*, Majid Kajbafzadeh, Zubair Hasan, Eugene Wong, Hasantha Gunasekera, Chris Perry, Raymond Sacks, Ashnil Kumar, Narinder Singh

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)
54 Downloads (Pure)


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 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 multiclassification algorithm (3 or more diagnostic classes) achieved 97.6% (95%CI: 97.3–97.9%) accuracy to differentiate between normal, AOM and OME in three studies. 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 three 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.

Original languageEnglish
Pages (from-to)401-413
Number of pages13
JournalClinical Otolaryngology
Issue number3
Early online date6 Mar 2022
Publication statusPublished - May 2022
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • artificial intelligence
  • computer vision
  • diagnosis
  • machine learning
  • otoscopy


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