Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

by   Xiang Bai, et al.

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.


page 21

page 22

page 23

page 24

page 25

page 26

page 27

page 28


The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond

Machine Learning (ML) and Artificial Intelligence (AI) have shown promis...

Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention

prevention of stroke with its associated risk factors has been one of th...

Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model

While developing artificial intelligence (AI)-based algorithms to solve ...

Federated Learning on Heterogenous Data using Chest CT

Large data have accelerated advances in AI. While it is well known that ...

Efficient Adaptive Federated Optimization of Federated Learning for IoT

The proliferation of the Internet of Things (IoT) and widespread use of ...

Social Metaverse: Challenges and Solutions

Social metaverse is a shared digital space combining a series of interco...

Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout

Artificial intelligence (AI)-empowered industrial fault diagnostics is i...

Please sign up or login with your details

Forgot password? Click here to reset