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An Artificial Intelligence Computer-vision Algorithm to Triage Otoscopic Images From Australian Aboriginal and Torres
Al-Rahim Habib1,2,3, Graeme Crossland4, Hemi Patel4
1Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia.
Summary
An artificial intelligence algorithm accurately classifies ear diseases in Indigenous Australian children using otoscopic images. This technology can improve early detection and treatment for remote communities lacking specialist care.
Area of Science:
- Medical imaging analysis
- Artificial intelligence in healthcare
- Otolaryngology
Background:
- Rural and remote Aboriginal and Torres Strait Islander children face limited access to specialist ear care.
- Under-identification of ear disease is a concern in these underserved populations.
- Telemedicine offers a potential solution for remote healthcare delivery.
Purpose of the Study:
- To develop an AI algorithm for classifying otoscopic images.
- To triage ear conditions in Indigenous Australian children from remote areas.
- To support early detection and management of ear disease.
Main Methods:
- Retrospective observational study using 6,527 otoscopic images.
- Deep and transfer learning methods for algorithm development.
- Otolaryngologist-labeled ground truth for algorithm validation.
Main Results:
- High accuracy achieved: 99.3% for acute otitis media, 96.3% for chronic otitis media, 98.2% for wax.
- The algorithm demonstrated strong performance in differentiating multiple diagnoses (AUC 0.963-0.997).
- Otitis media with effusion (OME) was the most common misclassification.
Conclusions:
- AI image classification can accurately identify ear disease in otoscopic images of Indigenous Australian children.
- Validated algorithms can enhance telemedicine initiatives for effective triage.
- Early treatment and referral can be facilitated, addressing disparities in ear health care.
