An artificial intelligence computer-vision algorithm to triage otoscopic images from Australian Aboriginal and Torres Strait Islander children
AR Habib, G Crossland, H Patel, E Wong… - Otology & …, 2022 - journals.lww.com
AR Habib, G Crossland, H Patel, E Wong, K Kong, H Gunasekera, B Richards, L Caffery…
Otology & Neurotology, 2022•journals.lww.comObjective: 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 …
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 …
Abstract
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.
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