ArticleDigital health
Ear-Keeper: A cross-platform artificial intelligence system for rapid and accurate ear disease diagnosis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- LUCID: Intelligent Informative Frame Selection in Otoscopy for Enhanced Diagnostic Utility.Diagnostics (Basel, Switzerland) · 2026Article
- Image-based deep learning in otology: a state-of-the-art review.Frontiers in neurology · 2026Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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 (22,581 images) and external (1,652 images) test sets, with subgroup analyses by age and gender. Grad-CAM visualizations were used to improve interpretability. A cross-platform intelligent diagnostic system, Ear-Keeper, was further developed for deployment on smartphones, tablets, and personal computers. Results: Best-EarNet achieved accuracies of 95.23% on the internal test set and 92.14% on the external test set, with a model size of 2.94 MB. It processed images at 80 frames per second on a standard CPU. Subgroup analyses demonstrated consistently high performance across age and gender groups. Grad-CAM visualizations highlighted lesion-related regions, and Ear-Keeper enabled real-time video-based ear screening across multiple platforms. Conclusion: Best-EarNet offers an accurate, efficient, and interpretable solution for ear disease classification. Its real-time performance and cross-platform deployment through Ear-Keeper support both clinical practice and community-level screening, with strong potential for early detection and intervention.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.