Accessible Otitis Media Screening with a Deep Learning-Powered Mobile Otoscope

Otitis media (OM) is the leading cause of hearing loss in children globally, affecting nearly a billion people per year. Impoverished areas typically lack trained ear specialists, which prevents millions from being diagnosed and treated while causing severe complications. Currently, there is no viable diagnostic system for inexpensively and accurately detecting such ear conditions. This research presents OtoScan, a novel pipeline for the detection of middle ear infections using diagnosis networks and a cost-effective mobile otoscope. The physical attachment was developed using custom-designed 3D models, a compact magnification lens, fiber optics, and various electronics for illumination. To develop detection algorithms, public otoscopic images were collected and augmented with realistic perturbations. A dynamic ensemble of Inception-based architectures trained using transfer learning and label smoothing was developed to mitigate class imbalance and overconfidence while improving diagnostic accuracy for acute and chronic suppurative OM. Regions of interest are highlighted as gradient saliency maps in a smartphone application using Grad-CAM++. Evaluation shows that the proposed algorithm surpasses architectures such as CBAM in accuracy and F1 score. Further testing using an industry-standard medical simulator validated the potential viability of this system. With a production cost of $9.50 USD, OtoScan represents a step towards the democratization of ear care and improvement of patient outcomes.