Abstract
Artificial intelligence provides a promising solution 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-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) 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 from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health.
Highlights
As the gold standard for identifying coronavirus disease 2019 (COVID-19) carriers, polymerase chain reaction with reverse transcription (RTâPCR) is the primary diagnostic modality to detect viral nucleotide in specimens from cases with suspected infection; due to the various disease courses in different patients, the detection sensitivity hovers at around only 0.60â0.71, which results in a considerable number of false negatives
We introduced a multination collaborative artificial intelligence (AI) framework, Unified computed tomography scans (CTs)-COVID AI Diagnostic Initiative (UCADI), to assist radiologists in streamlining and accelerating CT-based COVID-19 diagnoses
We formed a federated learning framework to enable the global training of a CT-based model for precise and robust diagnosis
Summary
As the gold standard for identifying coronavirus disease 2019 (COVID-19) carriers, polymerase chain reaction with reverse transcription (RTâPCR) is the primary diagnostic modality to detect viral nucleotide in specimens from cases with suspected infection; due to the various disease courses in different patients, the detection sensitivity hovers at around only 0.60â0.71 (refs. 1â4), which results in a considerable number of false negatives. Due to the different acquisition protocols (for example, contrast agents and reconstruction kernels), CTs collected from a single hospital are still not yet well standardized; it is challenging to train a precise model on the basis of a simple combination of data[17]. It remains an open question whether the patients with COVID-19 from diverse geographies and varying demographics show similar or distinct patterns. It is worth noting that these challenges are generally encountered by all of the possible trails in applying AI models in clinical practices, not necessarily COVID-19 related
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