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https://wslhd.intersearch.com.au/wslhdjspui/handle/1/4461
Title: | An Artificial Intelligence Computer-vision Algorithm to Triage Otoscopic Images From Australian Aboriginal and Torres Strait Islander Children |
Authors: | Habib, Al-Rahim;Crossland, G.;Patel, H.;Wong, E.;Kong, K.;Gunasekera, H.;Richards, B.;Caffery, L.;Perry, C.;Sacks, R.;Kumar, A.;Singh, Narinder |
WSLHD Author: | Habib, Al-Rahim;Singh, Narinder |
Issue Date: | 2022 |
Citation: | Otology & Neurotology 43(4):481-488, 2022 |
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. INTERVENTIONS: 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. |
URI: | https://wslhd.intersearch.com.au/wslhdjspui/handle/1/4461 |
DOI: | https://dx.doi.org/10.1097/MAO.0000000000003484 |
Journal: | Otology & Neurotology |
Type: | Journal Article |
Study or Trial: | Observational Study Research Support, Non-U.S. Gov't |
Facility: | Westmead |
Keywords: | Australia Computers Ear Diseases Native Hawaiian or Other Pacific Islander Otitis Media |
Appears in Collections: | Westmead Hospital 2019 - 2024 |
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