Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Abstract

Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

RETRACTED ARTICLE: Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment

“…The method is called FedPSO and increases its strength in unstable network platforms by transferring score values instead of larger weights. Dou et al ( 2021 ) determine the feasibility of FL technique to detect COVID-19-oriented CT abnormality with exterior authentication on persons from multi-national research.…”

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

“…significantly user privacy which is valuable in the pandemic [140]. Moreover, FL is also combined with DL to build a deep collaborative learning solution for detecting COVID-19 lung abnormalities in CT [141]. The internal datasets are collected from a total of 75 patients confirmed COVID-19 infection at three local hospitals in Hong Kong for FL simulations, and then the generalizability is validated on external cohorts from Mainland China and Germany.…”

“…At each hospital, a CNN model is trained by allowing each X-ray image to be put into a convolutional layer and output the probability of COVID-19 infection, and then a central server is used to aggregate synchronously with the local institutions for building a strong classification model for COVID- 19 detection without compromising significantly user privacy which is valuable in the pandemic [140] . Moreover, FL is also combined with DL to build a deep collaborative learning solution for detecting COVID-19 lung abnormalities in CT [141] . The internal datasets are collected from a total of 75 patients confirmed COVID-19 infection at three local hospitals in Hong Kong for FL simulations, and then the generalizability is validated on external cohorts from Mainland China and Germany.…”

Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease

“…Radiomics features can be further integrated into machine learning models with the aim to improve diagnosis and patient management. This approach was recently investigated to improve the detection and the differential diagnosis of COVID-19 pneumonia 20 , 21 , 23 , 24 , 26 – 29 . For example, Zhang et al proposed a CT-based deep learning integrated radiomics model for the differentiation of COVID-19 pneumonia from other community acquired pneumonias.…”