A collaborative online AI engine for CT-based COVID-19 diagnosis

Abstract

Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices. To overcome this, we proposed a federated learning-based Unified CT-COVID AI Diagnostic Initiative (UCADI, http://www.ai-ct-covid.team/), a decentralized architecture where the AI model is distributed to and executed at each host institution with the data sources or client ends for training and inferencing without sharing individual patient data. Specifically, we firstly developed an initial AI CT model based on data collected from three Tongji hospitals in Wuhan. After model evaluation, we found that the initial model can identify COVID from Tongji CT test data at near radiologist-level (97.5% sensitivity) but performed worse when it was tested on COVID cases from Wuhan Union Hospital (72% sensitivity), indicating a lack of model generalization. Next, we used the publicly available UCADI framework to build a federated model which integrated COVID CT cases from the Tongji hospitals and Wuhan Union hospital (WU) without transferring the WU data. The federated model not only performed similarly on Tongji test data but improved the detection sensitivity (98%) on WU test cases. The UCADI framework will allow participants worldwide to use and contribute to the model, to deliver a real-world, globally built and validated clinic CT-COVID AI tool. This effort directly supports the United Nations Sustainable Development Goals' number 3, Good Health and Well-Being, and allows sharing and transferring of knowledge to fight this devastating disease around the world.

Figure 1 |. The conceptual architecture of UCADI on the basis of federated learning.

UCADI stakeholders firstly download the code and train a new model locally based on the initial model, and secondly transfer the encrypted model parameters back to the federated model. The central server combines the contributions shared from all of the UCADI participants.

Figure 2 |. Data and strategy.

a, number of CT studies and total images. b, the CNN was developed based on 3D-Densenet, consisting of 6 dense blocks in green, 2 transmit blocks in white and an output layer in gray. Pre-processed 128-x-128-pixel CT images of one case were fed to the network across 14 3D-convolution layers and a number of functions embedded in 3D blocks, finally received the predicted classification result. c, the CNN classified CT case into 4 types and further assessed the severity into I or II or III if the case was predicted as COVID-19.

Figure 3 |. CT images. i and ii, the taxonomy of pneumonia and featured CT image for per-class. iii, the heatmap generated by GradCAM and local lesions annotated by the radiologist.

i, COVID-19 pneumonia. a, b, c represent the CT images of COVID-19 defined by radiological features. ii, non-COVID-19 cases. d, e, f respectively displays the CT image of healthy case, other viral pneumonia, and bacterial pneumonia. iii, CAM visualized the image areas which lead to classification decision. The radiologist, LYM [9 years’ experience], from Department of Radiology, Tongji Hospital circumscribed the local lesions with the red curved masks. g-h, patients with COVID-19 pneumonia.

Figure 4|. Pneumonia classification performance of CNN models and radiologists.

This figure illustrates the comparative analysis between the CNN and radiologists by correlating the ROC curve of CNN and sensitivity-specificity points of six invited radiologists for two conducted classification test tasks. a-d, per-class evaluation for three types of pneumonia and healthy case. The curve in black represents the performance of the CNN. Cross marks in red separately represent the performance of six radiologists and the blue mark annotates the average capability. e-h, comparative evaluation of centralized-trained initial model, federated model, and Tongji Main Campus model on four per-class classification tasks.