Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies

(2024) 24:123 Alrashdi BMC Medical Imaging https://doi.org/10.1186/s12880-024-01302-8 BMC Medical Imaging Open Access RESEARCH Fog‑based deep learning framework for real‑time pandemic screening in smart cities from multi‑site tomographies Ibrahim Alrashdi1* Abstract The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of artificial intelligence (AI) with the Internet of Things (IoT), is commonly used to promote efficient control and diagnosis during the outbreak, thereby minimizing possible losses. However, the presence of multi-source institutional data remains one of the major challenges hindering the practical usage of AIoT solutions for pandemic disease diagnosis. This paper presents a novel framework that utilizes multi-site data fusion to boost the accurateness of pandemic disease diagnosis. In particular, we focus on a case study of COVID-19 lesion segmentation, a crucial task for understanding disease progression and optimizing treatment strategies. In this study, we propose a novel multidecoder segmentation network for efficient segmentation of infections from cross-domain CT scans in smart cities. The multi-decoder segmentation network leverages data from heterogeneous domains and utilizes strong learning representations to accurately segment infections. Performance evaluation of the multi-decoder segmentation network was conducted on three publicly accessible datasets, demonstrating robust results with an average dice score of 89.9% and an average surface dice of 86.87%. To address scalability and latency issues associated with centralized cloud systems, fog computing (FC) emerges as a viable solution. FC brings resources closer to the operator, offering low latency and energy-efficient data management and processing. In this context, we propose a unique FC technique called PANDFOG to deploy the multi-decoder segmentation network on edge nodes for practical and clinical applications of automated COVID-19 pneumonia analysis. The results of this study highlight the efficacy of the multidecoder segmentation network in accurately segmenting infections from cross-domain CT scans. Moreover, the proposed PANDFOG system demonstrates the practical deployment of the multi-decoder segmentation network on edge nodes, providing real-time access to COVID-19 segmentation findings for improved patient monitoring and clinical decision-making. Keywords Smart Cities, Pandemic diseases, Fog computing, Deep learning, Internet of Things (IoT) *Correspondence: Ibrahim Alrashdi 1 Department of Computer Science, College of Computer and Information Sciences, Jouf University, 72388 Sakaka, Aljouf, Saudi Arabia Introduction Pandemic diseases have become increasingly confronting for public infrastructure globally, with their extensive transmission and severe effects on individuals and communities. The rapid and perfect diagnosis of these diseases is of paramount importance for effective control and mitigation strategies [1]. The landscape of healthcare technology has been encountering a revolutionary shift © The Author(s) 2024. 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The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Alrashdi B MC Medical Imaging (2024) 24:123 in the wake of the COVID-19 pandemic, which highlighted the serious need for improved and adaptive solutions that can provide rapid and accurate diagnosis of pandemic diseases, particularly in urban environments where population density and mobility amplify the challenges of pandemic management [2]. Smart cities, the epitome of urban innovation, demonstrate the revolutionary role of integrating technologies in urban management. Specifically, the recent challenges modeled by the COVID-19 pandemic have prompted the conjunction of smart city technologies and pandemic control mechanisms. The process of screening pandemic disease is an essential element of public health surveillance and is now being reimagined and sustained through the application of cutting-edge technologies including real-time data analytics, predictive analytics, and fast reply apparatuses are at the vanguard of this evolving method [2]. Leading nations in this new paradigm of pandemic containment in smart cities have been identified. For instance, Singapore has put in place a national contact tracing app that uses Bluetooth technology to find and notify anyone who might have come into contact with COVID-19 cases that have been confirmed. Efficient control of outbreaks has been made possible by South Korea’s strong IT infrastructure, vigorous testing, and data-sharing attitude [3]. Furthermore, Taiwan’s creative integration of medical records and travel history to identify possible cases was partly responsible for the pandemic’s successful containment. These examples demonstrate how smart city technologies can redefine the parameters and extent of pandemic control. As countries throughout the world struggled to contain the outbreak, smart cities and the incorporation of internet technologies showed promise as a way to improve healthcare delivery and reaction times [4, 5]. The necessity of utilizing data-rich surroundings to promote accurate illness diagnosis and proactive decision-making in urban settings is now more important than ever in the post-COVID era. Smart cities have developed as centers of innovation that harness new technologies to address public health concerns. Specifically, the Artificial Intelligence of Things (AIoT) technology has developed as the result of the convergence between artificial intelligence (AI) and the Internet of Things (IoT) to offer a new paradigm to control pandemic diseases based on the data distributed across different geographical locations [6, 7]. The adoption of the AIoT framework in smart cities represents a paradigm shift in the way public health challenges are addresse (...truncated)