An Explainable Artificial Intelligence-Based Robustness Optimization Approach for Age-Related Macular Degeneration Detection Based on Medical IOT Systems

Abstract

AI-based models have shown promising results in diagnosing eye diseases based on multi-sources of data collected from medical IOT systems. However, there are concerns regarding their generalization and robustness, as these methods are prone to overfitting specific datasets. The development of Explainable Artificial Intelligence (XAI) techniques has addressed the black-box problem of machine learning and deep learning models, which can enhance interpretability and trustworthiness and optimize their performance in the real world. Age-related macular degeneration (AMD) is currently the primary cause of vision loss among elderly individuals. In this study, XAI methods were applied to detect AMD using various ophthalmic imaging modalities collected from medical IOT systems, such as colorful fundus photography (CFP), optical coherence tomography (OCT), ultra-wide fundus (UWF) images, and fluorescein angiography fundus (FAF). An optimized deep learning (DL) model and novel AMD identification systems were proposed based on the insights extracted by XAI. The findings of this study demonstrate that XAI not only has the potential to improve the transparency, reliability, and trustworthiness of AI models for ophthalmic applications, but it also has significant advantages for enhancing the robustness performance of these models. XAI could play a crucial role in promoting intelligent ophthalmology and be one of the most important techniques for evaluating and enhancing ophthalmic AI systems.

Advancements in Demodex mite detection: a comparative analysis of YOLOv5 and YOLOv8 utilizing microscopic examination images

“…Integrating AI-based detection methods seamlessly into existing clinical workflows, ensuring interoperability, and addressing ethical considerations pertaining to privacy and informed consent are additional challenges. Moreover, the interpretability and explainability of AI models 12 , particularly in the context of dermatological applications, demand careful attention to foster trust among healthcare professionals. Meeting real-time processing requirements, adapting to diverse hardware configurations, and handling variations in image quality represent technical challenges.…”