- Title
- An Artificial Intelligence Computer-vision Algorithm to Triage Otoscopic Images From Australian Aboriginal and Torres Strait Islander Children
- Creator
- Habib, Al-Rahim; Crossland, Graeme; Kumar, Ashnil; Singh, Narinder; Patel, Hemi; Wong, Eugene; Kong, Kelvin; Gunasekera, Hasantha; Richards, Brent; Caffery, Liam; Perry, Chris; Sacks, Raymond
- Relation
- Otology & Neurotology Vol. 43, Issue 4, p. 481-488
- Publisher Link
- http://dx.doi.org/10.1097/MAO.0000000000003484
- Publisher
- Lippincott Williams & Wilkins
- Resource Type
- journal article
- Date
- 2022
- Description
- Objective: To develop an artificial intelligence image classification algorithm to triage otoscopic images from rural and remote Australian Aboriginal and Torres Strait Islander children. Study Design: Retrospective observational study. Setting: Tertiary referral center. Patients: Rural and remote Aboriginal and Torres Strait Islander children who underwent tele-otology ear health screening in the Northern Territory, Australia between 2010 and 2018. Intervention(s): Otoscopic images were labeled by otolaryngologists to classify the ground truth. Deep and transfer learning methods were used to develop an image classification algorithm. Main Outcome Measures: Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, area under the curve (AUC) of the resultant algorithm compared with the ground truth. Results: Six thousand five hundred twenty seven images were used (5927 images for training and 600 for testing). The algorithm achieved an accuracy of 99.3% for acute otitis media, 96.3% for chronic otitis media, 77.8% for otitis media with effusion (OME), and 98.2% to classify wax/obstructed canal. To differentiate between multiple diagnoses, the algorithm achieved 74.4 to 92.8% accuracy and an AUC of 0.963 to 0.997. The most common incorrect classification pattern was OME misclassified as normal tympanic membranes. Conclusions: The paucity of access to tertiary otolaryngology care for rural and remote Aboriginal and Torres Strait Islander communities may contribute to an under-identification of ear disease. Computer vision image classification algorithms can accurately classify ear disease from otoscopic images of Indigenous Australian children. In the future, a validated algorithm may integrate with existing telemedicine initiatives to support effective triage and facilitate early treatment and referral.
- Subject
- artificial intelligence; computer-vision; deep learning; image classification; machine learning; otitis media
- Identifier
- http://hdl.handle.net/1959.13/1464441
- Identifier
- uon:46998
- Identifier
- ISSN:1531-7129
- Language
- eng
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