Real-Time Ear Lesion Diagnosis Using an Ultralight and Ultrafast Deep Learning Model and a Large-Scale Ear Endoscopic Dataset

Khái niệm cốt lõi

The authors developed an ultrafast and ultralight deep learning model called Best-EarNet that achieves state-of-the-art performance in diagnosing eight types of ear diseases and normal ears. Best-EarNet is designed for real-world deployment, balancing diagnosis accuracy, inference speed, and model parameter size.

Tóm tắt

The authors first constructed the largest dataset of ear endoscopic images to date, comprising 24,233 images across 9 categories (8 ear diseases and normal ears). They then proposed a novel deep learning model called Best-EarNet, which is based on the lightweight ShuffleNetV2 architecture. Best-EarNet incorporates a Local-Global Spatial Feature Fusion Module and a multi-scale supervision strategy to effectively extract global and local features from the input images.
Through extensive experiments, the authors demonstrated that Best-EarNet achieves state-of-the-art performance, with an accuracy of 95.23% on the internal dataset and 92.14% on the external dataset. Importantly, Best-EarNet has an extremely small model size of only 0.77M parameters and can achieve an average inference speed of 80 FPS on a CPU, making it highly suitable for real-world deployment on resource-constrained devices.
The authors further validated the model's performance across different gender and age groups, as well as on an external dataset, to ensure its clinical applicability and generalization capability. They also employed Grad-CAM to visualize the model's decision-making process, providing transparency and interpretability.
Finally, the authors developed four versions of the "Ear-Keeper" application based on Best-EarNet, targeting different user scenarios, including mobile self-diagnosis, community screening, and specialized otolaryngology practices. These applications aim to assist the public and healthcare providers in the early detection and treatment of ear diseases, reducing misdiagnosis and improving diagnosis efficiency.

Thống kê

The dataset used in this study consists of 24,233 ear endoscopic images, including 451 Acute Otitis Media (AOM), 565 Cholesteatoma of Middle Ear (CME), 4,135 Chronic Suppurative Otitis Media (CSOM), 556 External Auditory Canal Bleeding (EACB), 6,485 Impacted Cerumen (IC), 5,399 Normal Eardrum (NE), 2,604 Otomycosis External (OE), 2,811 Secretory Otitis Media (SOM), and 1,227 Tympanic Membrane Calcification (TMC) samples.

Trích dẫn

"Ear-Keeper, an intelligent diagnosis system based Best-EarNet, was developed successfully and deployed on common electronic devices (smartphone, tablet computer and personal computer)."
"In the future, Ear-Keeper has the potential to assist the public and healthcare providers in performing comprehensive scanning and diagnosis of the ear canal in real-time video, thereby promptly detecting ear lesions."

Thông tin chi tiết chính được chắt lọc từ

Ear-Keeper

by Yubiao Yue,X... lúc arxiv.org 04-11-2024

https://arxiv.org/pdf/2308.10610.pdf

Yêu cầu sâu hơn

How can the Ear-Keeper application be further improved to enhance user experience and engagement, such as through the integration of educational content or personalized recommendations?

To enhance the user experience and engagement of the Ear-Keeper application, several improvements can be implemented. Firstly, integrating educational content within the application can provide users with valuable information about ear health, common ear diseases, and preventive measures. This educational content can be in the form of articles, videos, or interactive modules to educate users about ear care.
Personalized recommendations can also be incorporated into the application to provide tailored suggestions based on the user's history, preferences, and diagnostic results. For example, the application can recommend specific ear care practices, follow-up appointments, or lifestyle changes based on the user's diagnosis and health profile. Personalization can enhance user engagement and encourage users to take proactive steps towards better ear health.
Furthermore, incorporating features such as reminders for follow-up appointments, medication schedules, and regular ear check-ups can help users stay on track with their ear health management. Gamification elements, such as rewards for completing diagnostic tasks or educational quizzes, can make the user experience more interactive and enjoyable.

What are the potential challenges and ethical considerations in deploying an AI-powered ear disease diagnosis system, and how can they be addressed to ensure responsible and equitable healthcare delivery?

Deploying an AI-powered ear disease diagnosis system comes with several challenges and ethical considerations. One challenge is ensuring the accuracy and reliability of the AI model, as any errors in diagnosis can have serious consequences for patients. It is essential to continuously validate and update the model with new data to improve its performance and reliability.
Ethical considerations include issues related to patient privacy, data security, and informed consent. Healthcare providers must ensure that patient data is protected and used responsibly in compliance with data protection regulations. Transparent communication with patients about the use of AI in diagnosis, the limitations of the technology, and the potential risks is crucial to building trust and ensuring informed decision-making.
Another challenge is ensuring equitable access to AI-powered diagnosis systems, especially in underserved communities or regions with limited healthcare resources. Efforts should be made to address disparities in access to technology and healthcare services to ensure that all individuals have equal opportunities to benefit from AI-powered diagnostics.
To address these challenges and ethical considerations, healthcare providers can implement robust data governance policies, conduct regular audits of the AI system, provide comprehensive training to healthcare professionals using the technology, and engage in ongoing discussions with patients and stakeholders to address concerns and ensure responsible and equitable healthcare delivery.

Given the success of the Best-EarNet model in ear disease diagnosis, how can the transfer learning and feature fusion techniques used in this work be applied to improve the performance of deep learning models in other medical imaging domains?

The transfer learning and feature fusion techniques used in the Best-EarNet model can be applied to improve the performance of deep learning models in other medical imaging domains by leveraging existing knowledge and optimizing feature extraction.
In transfer learning, pre-trained models can be fine-tuned on new medical imaging datasets to adapt to specific tasks and domains. By transferring knowledge learned from one dataset to another, models can achieve better performance with less data and training time. This approach can be particularly beneficial in medical imaging, where labeled data is often limited and expensive to acquire.
Feature fusion techniques, such as the Local-Global Spatial Feature Fusion Module used in Best-EarNet, can enhance the model's ability to capture both local and global information in medical images. By combining features at different levels of abstraction, models can extract more relevant and discriminative information, leading to improved diagnostic accuracy.
These techniques can be applied to various medical imaging tasks, such as detecting tumors in radiology images, identifying skin lesions in dermatology images, or analyzing retinal scans for diabetic retinopathy. By incorporating transfer learning and feature fusion into deep learning models in other medical imaging domains, researchers and healthcare providers can develop more accurate, efficient, and reliable diagnostic tools for improved patient care and outcomes.