Clinical Solutions

Coronavirus disease (COVID-19) is an infectious disease caused by a new virus that had not been previously identified in humans. The imaging appearance of COVID-19 in radiographic and CT indicates severe damage to Lungs, however, this appearance is not specific to the disease and can be seen with other infections, too.

Our Data Scientists and AI Engineers have a solid record in using Deep Learning technology for developing software which detects any pattern and feature in datasets.

In our platform, we implement Deep learning technology, a subfield within Artificial Intelligence which has done remarkably well in image classification, segmentation and processing tasks in past years. Our proposed Software contains multiple convolutional neural networks (CNN), which are collectively called PolyNet. PolyNet inputs a chest X-ray image and outputs the probability of pneumonia along with a heatmap localizing the areas of the image most indicative of pneumonia. We construct the initial network to make one of the following four predictions: a) no infection (normal), b) bacterial infection, c) non-COVID viral infection, and d) COVID-19 viral infection. The rationale for choosing these four possible predictions is that it can aid clinicians to better decide who should be prioritized for PCR testing.

It is most crucial in this pandemic to predict the possible procedure for treating patients with Covid-19 fast and efficient based on the severity of infection, therefore we use Life-Long Deep Neural Network (L-DNN) in order to enable our software to continue learning from radiologists while being in practice. The software will be able to suggest measures after recognising the Covid-19 Pneumonia and the severity of the infection in the Lungs. The suggested procedures comply with the guideline provided by the Robert Koch Institute.

We use Deep learning technology, a subfield within Artificial Intelligence which has done remarkably well in image classification, segmentation and processing tasks. Our proposed Software contains multiple convolutional neural networks (CNN), which collectively call them PolyNet. We construct the initial network design prototype to make one of the following four predictions: a) no infection (normal), b) bacterial infection, c) non-COVID viral infection, and d) COVID-19 viral infection. The rationale for choosing these four possible predictions is that it can aid clinicians to better decide not only who should be prioritized for PCR testing for COVID-19 case confirmation. PolyNet inputs a chest X-ray image and outputs the probability of pneumonia along with a heatmap localizing the areas of the image most indicative of pneumonia.

A significant subject for improving artificial intelligence is continual learning, or lifetime learning, the ability to master successive tasks without forgetting how to execute previously learned tasks. The primary goal of continual learning is to overcome the forgetting of learned tasks and to leverage the earlier knowledge for obtaining better performance or faster convergence/training speed on the newly coming tasks. According to our Radiology Advisory at medical school of Hannover), it is crucial and most needed in this pandemic to predict the possible procedure for treating patients with Covid-19 fast and efficient, therefore we use Life-Long Deep Neural Network in order to enable our software to continue learning from Radiologists while being in practice. The software will be able to suggest measures after recognising the Covid-19 Pneumonia and the severity of the infection in the Lungs. The suggested procedures comply with the guideline provided by Robert Koch Institute.

We aim to develop the software further and train similar models with CT-scan images so the software will be able to perform same tasks for CT-scan images as input as well.

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