Ear-Keeper: A cross-platform artificial intelligence system for rapid and accurate ear disease diagnosis.

ABSTRACT:

Objective

Early and accurate detection of ear diseases is essential for preventing hearing impairment and improving population health. This study aimed to develop a lightweight, high-performance, and real-time deep learning model for otoscopic image classification and to deploy it in a cross-platform diagnostic system for clinical and community use.

Methods

We constructed a large-scale, multi-center otoscopy dataset covering eight common ear diseases and healthy cases. Based on this dataset, we developed Best-EarNet, an ultrafast lightweight architecture integrating a local-global spatial feature fusion module and a multi-scale supervision strategy to enhance feature representation. Transfer learning was applied to optimize performance. The model was evaluated on internal (2

SUBMITTER: Lu F 

PROVIDER: S-EPMC12775339 | biostudies-literature | 2026 Jan-Dec

REPOSITORIES: biostudies-literature