RAIDER: Rapid AI Diagnosis at Edge Using Ensemble Models for Radiology

Abstract

Chest X-rays have played an indispensable part in medical diagnosis for several decades. However, there is a scarcity of experts who can interpret these images to diagnose critical illnesses, which can lead to preventable fatalities. This paper introduces a novel Rapid AI Diagnosis at Edge using Ensemble Models for Radiology (RAIDER) designed to leverage the advantages of cross-geolocation meta-learning models. We can generate local machine learning models at individual locations and distribute them across other locations for diagnosing diseases at the edge or on-premises if required before they become worldwide pandemics, significantly enhancing the rapid or near-real-time identification of fast-spreading respiratory diseases through online learning. This novel approach allows for geo-distributed multi-fold model training, harnessing the unique strengths of diverse geographical data sources to improve diagnostic accuracy and speed by leveraging edge computing. Using the existing Convolutional Neural Network (CNN) models and distributed training at the edge, we can enhance the accuracy and cost-effectiveness of diagnosis. The proposed architecture allows for distributed training and independently verified performance metrics on the MIMIC-CXR and COVIDGR chest X-ray datasets with accuracy, sensitivity, specificity, F1-score and AUC of 97.80%, 97.06%, 98.48%, 96.51%, and 0.9739, respectively. Our proposed RAIDER architecture marks the first implementation of a collaborative framework that facilitates seamless interaction across different geographic locations and edge computing, enabling a more effective and efficient response to emerging health threats.

Keywords

• Chest x-ray
• medical image diagnosis
• edge computing
• ensemble learning
• meta-learning