Ear-Keeper: A cross-platform artificial intelligence system for rapid and accurate ear disease diagnosis
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By
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Feiyan Lu
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Yubiao Yue
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Zhenzhang Li
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Meiping Zhang
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Wen Luo
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Fan Zhang
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Tong Liu
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Jingyong Shi
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Guang Wang
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Xinyu Zeng
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January 6, 2026
Clinical Scorecard: AI-Driven Ear Diagnostic System: A Cross-Platform Approach for Swift and Precise Identification of Ear Disorders
At a Glance
| Category | Detail |
|---|
| Condition | Ear Disorders |
| Key Mechanisms | AI-driven diagnostic system utilizing deep learning for image recognition and analysis. |
| Target Population | Individuals with ear disorders, particularly children in developing countries. |
| Care Setting | Community clinics, primary care, telemedicine, and resource-limited areas. |
Key Highlights
- Over 500 million people affected by ear infections annually.
- Otitis media is the most common ear disease, leading to significant healthcare visits.
- AI model Best-EarNet achieves a good trade-off between diagnosis performance and inference speed.
- Ear-Keeper application supports self-diagnosis and professional diagnosis across various devices.
- Timely detection and treatment can prevent complications and hearing loss.
Guideline-Based Recommendations
Diagnosis
- Utilize AI-driven models for accurate identification of ear disorders.
Management
- Implement timely treatment to prevent complications associated with ear diseases.
Monitoring & Follow-up
- Regular check-ups using mobile applications for self-diagnosis.
Risks
- Misdiagnosis in nonspecialty clinics can lead to severe complications.
Patient & Prescribing Data
Children and individuals in resource-limited settings.
AI applications can facilitate early detection and reduce reliance on broad-spectrum antibiotics.
Clinical Best Practices
- Incorporate AI diagnostic tools in routine ear examinations.
- Ensure accessibility of ear healthcare services in underserved areas.
- Utilize mobile technology for patient self-assessment.
Related Resources & Content
